Reading the repo
Reading the repo
How to install on Windows
The most powerful local music generation model that outperforms almost all commercial alternatives, supporting Mac, AMD, Intel, and CUDA devices.
Saved setup from 4 h ago
The maintainers included install_uv.bat. Run it from the cloned folder after reading it.
.\install_uv.bat
Also: setup_xpu.bat, install_uv.sh, run_api_server.sh, run_openrouter_api_server.sh
Will it work on my device?
winget install Git.Git) or WSL2 (wsl --install).Then run powershell -ExecutionPolicy Bypass -File .\setup.ps1. It stops at the first error and is safe to run again. Servers and tests aren't started; it lists them at the end. Read it first.
Needs Git: winget install Git.Git
git clone https://github.com/ace-step/ACE-Step-1.5.git cd ACE-Step-1.5
Node.js 20, Python >=3.11,<3.13
winget install CoreyButler.NVMforWindows nvm install 20 nvm use 20 winget install astral-sh.uv
Copy-Item .env.example .env
npm install uv sync
The steps run shell scripts (.sh). Use Git Bash (winget install Git.Git) or WSL2 (wsl --install).
🎵 Don't want to install locally? Try acemusic.ai — 100% free, no GPU required!
Requirements: Python 3.11-3.12, CUDA GPU recommended (also supports MPS / ROCm / Intel XPU / CPU)
Note: ROCm on Windows requires Python 3.12 (AMD officially provides Python 3.12 wheels only)
# 1. Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh # macOS / Linux
# powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" # Windows
# 2. Clone & install
git clone https://github.com/ACE-Step/ACE-Step-1.5.git
cd ACE-Step-1.5
uv sync
# 3. Launch Gradio UI (models auto-download on first run)
uv run acestep
# Or launch REST API server
uv run acestep-api
Open http://localhost:7860 (Gradio) or http://localhost:8001 (API).
📦 Windows users: A portable package with pre-installed dependencies is available. See Installation Guide.
📦 MacOS users: A portable package with pre-installed dependencies is available. See Installation Guide.
📖 Full installation guide (AMD/ROCm, Intel GPU, CPU, environment variables, command-line options): English | 中文 | 日本語
| Your GPU VRAM | Recommended DiT | Recommended LM Model | Backend | Notes |
|---------------|----------------|---------------------|---------|-------|
| ≤6GB | 2B turbo | None (DiT only) | — | LM disabled by default; INT8 quantization + full CPU offload |
| 6-8GB | 2B turbo | acestep-5Hz-lm-0.6B | pt | Lightweight LM with PyTorch backend |
| 8-16GB | 2B turbo/sft | acestep-5Hz-lm-0.6B / 1.7B | vllm | 0.6B for 8-12GB, 1.7B for 12-16GB |
| 16-20GB | 2B sft or XL turbo | acestep-5Hz-lm-1.7B | vllm | XL requires CPU offload below 20GB |
| 20-24GB | XL turbo/sft | acestep-5Hz-lm-1.7B | vllm | XL fits without offload; 4B LM available |
| ≥24GB | XL sft (or xl-base for extract/lego/complete) | acestep-5Hz-lm-4B | vllm | Best quality, all models fit without offload |
XL (4B) models (
acestep-v15-xl-*) offer higher audio quality with ~9GB VRAM for weights (vs ~4.7GB for 2B). They require ≥12GB VRAM (with offload + quantization) or ≥20GB (without offload). All LM models are fully compatible with XL.
The UI automatically selects the best configuration for your GPU. All settings (LM model, backend, offloading, quantization) are tier-aware and pre-configured.
No reports for Windows yet.
Opens a chat with this manual already filled in, for Windows. It uses your own account; gitmanual sends nothing.