ACE-Step 1.5 Installation Guide

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Requirements
Quick Start (All Platforms)
Launch Scripts
Windows Portable Package
macOS Portable Package
AMD / ROCm GPUs
Intel GPUs
CPU-Only Mode
Linux Notes
Environment Variables (.env)
Command Line Options
Model Download
Which Model Should I Choose?
Development

Requirements

Item	Requirement
Python	3.11-3.12 (stable release, not pre-release) Note: ROCm on Windows requires Python 3.12
GPU	CUDA GPU recommended; MPS / ROCm / Intel XPU / CPU also supported
VRAM	≥4GB for DiT-only mode; ≥6GB for LLM+DiT
Disk	~10GB for core models

Quick Start (All Platforms)

1. Install uv (Package Manager)

bash

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone & Install

bash

git clone https://github.com/ACE-Step/ACE-Step-1.5.git
cd ACE-Step-1.5
uv sync

3. Launch

Gradio Web UI (Recommended):

bash

uv run acestep

REST API Server:

bash

uv run acestep-api

Using Python directly (Conda / venv / system Python):

bash

# Activate your environment first, then:
python acestep/acestep_v15_pipeline.py          # Gradio UI
python acestep/api_server.py                     # REST API

Models are downloaded automatically on first run. Open http://localhost:7860 (Gradio) or http://localhost:8001 (API).

🚀 Launch Scripts

Ready-to-use launch scripts for all platforms. These scripts handle environment detection, dependency installation, and application startup automatically. All scripts check for updates on startup by default (configurable).

Available Scripts

Platform	Script	Description
Windows	`start_gradio_ui.bat`	Launch Gradio Web UI (CUDA)
Windows	`start_api_server.bat`	Launch REST API Server (CUDA)
Windows	`start_gradio_ui_rocm.bat`	Launch Gradio Web UI (AMD ROCm)
Windows	`start_api_server_rocm.bat`	Launch REST API Server (AMD ROCm)
Linux	`start_gradio_ui.sh`	Launch Gradio Web UI (CUDA)
Linux	`start_api_server.sh`	Launch REST API Server (CUDA)
macOS	`start_gradio_ui_macos.sh`	Launch Gradio Web UI (MLX)
macOS	`start_api_server_macos.sh`	Launch REST API Server (MLX)

Windows

bash

# Launch Gradio Web UI (NVIDIA CUDA)
start_gradio_ui.bat

# Launch REST API Server (NVIDIA CUDA)
start_api_server.bat

# Launch Gradio Web UI (AMD ROCm)
start_gradio_ui_rocm.bat

# Launch REST API Server (AMD ROCm)
start_api_server_rocm.bat

ROCm users: The ROCm scripts (start_gradio_ui_rocm.bat, start_api_server_rocm.bat) auto-set HSA_OVERRIDE_GFX_VERSION, ACESTEP_LM_BACKEND=pt, MIOPEN_FIND_MODE=FAST and other ROCm-specific environment variables. They use a separate venv_rocm virtual environment to avoid CUDA/ROCm wheel conflicts.

Linux

bash

# Make executable (first time only)
chmod +x start_gradio_ui.sh start_api_server.sh

# Launch Gradio Web UI
./start_gradio_ui.sh

# Launch REST API Server
./start_api_server.sh

Note: Git must be installed via your system package manager (sudo apt install git, sudo yum install git, sudo pacman -S git).

macOS (Apple Silicon / MLX)

macOS scripts use the MLX backend for native Apple Silicon acceleration (M1/M2/M3/M4).

bash

# Make executable (first time only)
chmod +x start_gradio_ui_macos.sh start_api_server_macos.sh

# Launch Gradio Web UI with MLX backend
./start_gradio_ui_macos.sh

# Launch REST API Server with MLX backend
./start_api_server_macos.sh

The macOS scripts automatically set ACESTEP_LM_BACKEND=mlx and --backend mlx for native Apple Silicon acceleration, and fall back to PyTorch backend on non-arm64 machines.

Note: Install git via xcode-select --install or brew install git.

Script Features

Startup update check (enabled by default, configurable)
Auto environment detection (portable Python or uv)
Auto install uv if needed
Configurable download source (HuggingFace/ModelScope)
Customizable models and parameters

How to Modify Configuration

All configurable options are defined as variables at the top of each script. To customize, open the script with a text editor and modify the variable values.

Example: Change UI language to Chinese and use the 1.7B LM model

Windows (.bat) Linux / macOS (.sh)

Windows (.bat)	Linux / macOS (.sh)
Find these lines in `start_gradio_ui.bat`: batch `set LANGUAGE=en set LM_MODEL_PATH=--lm_model_path acestep-5Hz-lm-0.6B` Change to: batch `set LANGUAGE=zh set LM_MODEL_PATH=--lm_model_path acestep-5Hz-lm-1.7B`	Find these lines in `start_gradio_ui.sh`: bash `LANGUAGE="en" LM_MODEL_PATH="--lm_model_path acestep-5Hz-lm-0.6B"` Change to: bash `LANGUAGE="zh" LM_MODEL_PATH="--lm_model_path acestep-5Hz-lm-1.7B"`

Find these lines in start_gradio_ui.bat:

batch

set LANGUAGE=en
set LM_MODEL_PATH=--lm_model_path acestep-5Hz-lm-0.6B

Change to:

batch

set LANGUAGE=zh
set LM_MODEL_PATH=--lm_model_path acestep-5Hz-lm-1.7B

Find these lines in start_gradio_ui.sh:

bash

LANGUAGE="en"
LM_MODEL_PATH="--lm_model_path acestep-5Hz-lm-0.6B"

Change to:

bash

LANGUAGE="zh"
LM_MODEL_PATH="--lm_model_path acestep-5Hz-lm-1.7B"

Example: Disable startup update check

Windows (.bat)	Linux / macOS (.sh)
batch `REM set CHECK_UPDATE=true set CHECK_UPDATE=false`	bash `# CHECK_UPDATE="true" CHECK_UPDATE="false"`

Example: Enable a commented-out option — remove the comment prefix (REM for .bat, # for .sh):

Windows (.bat) Linux / macOS (.sh)

Windows (.bat)	Linux / macOS (.sh)
Before: batch `REM set SHARE=--share` After: batch `set SHARE=--share`	Before: bash `# SHARE="--share"` After: bash `SHARE="--share"`

Before:

batch

REM set SHARE=--share

After:

batch

set SHARE=--share

Before:

bash

# SHARE="--share"

After:

bash

SHARE="--share"

Common configurable options:

Option	Gradio UI	API Server	Description
`LANGUAGE`	✅	—	UI language: `en`, `zh`, `he`, `ja`
`PORT`	✅	✅	Server port (default: 7860 / 8001)
`SERVER_NAME` / `HOST`	✅	✅	Bind address (`127.0.0.1` or `0.0.0.0`)
`CHECK_UPDATE`	✅	✅	Startup update check (`true` / `false`)
`CONFIG_PATH`	✅	—	DiT model (`acestep-v15-turbo`, etc.)
`LM_MODEL_PATH`	✅	✅	LM model (`acestep-5Hz-lm-0.6B` / `1.7B` / `4B`)
`DOWNLOAD_SOURCE`	✅	✅	Download source (`huggingface` / `modelscope`)
`SHARE`	✅	—	Create public Gradio link
`INIT_LLM`	✅	—	Force LLM on/off (`true` / `false` / `auto`)
`OFFLOAD_TO_CPU`	✅	—	CPU offload for low-VRAM GPUs

Update & Maintenance Tools

Script (Windows)	Script (Linux/macOS)	Purpose
`check_update.bat`	`check_update.sh`	Check and update from GitHub
`merge_config.bat`	`merge_config.sh`	Merge backed-up configurations after update
`install_uv.bat`	`install_uv.sh`	Install uv package manager
`quick_test.bat`	`quick_test.sh`	Test environment setup

Update workflow:

bash

# Windows                          # Linux / macOS
check_update.bat                    ./check_update.sh
merge_config.bat                    ./merge_config.sh

🪟 Windows Portable Package

For Windows users, we provide a portable package with pre-installed dependencies:

Download and extract: ACE-Step-1.5.7z
The package includes python_embedded with all dependencies pre-installed
Requirements: CUDA 12.8

Quick Start Scripts

Script	Description
`start_gradio_ui.bat`	Launch Gradio Web UI
`start_api_server.bat`	Launch REST API Server

Both scripts support auto environment detection, auto uv install, configurable download source, optional Git update check, and customizable models/parameters.

Configuration

start_gradio_ui.bat:

batch

REM UI language (en, zh, he, ja)
set LANGUAGE=zh

REM Download source (auto, huggingface, modelscope)
set DOWNLOAD_SOURCE=--download-source modelscope

REM Git update check (true/false)
set CHECK_UPDATE=true

REM Model configuration
set CONFIG_PATH=--config_path acestep-v15-turbo
set LM_MODEL_PATH=--lm_model_path acestep-5Hz-lm-1.7B

start_api_server.bat:

batch

REM LLM initialization via environment variable
REM set ACESTEP_INIT_LLM=true   # Force enable LLM
REM set ACESTEP_INIT_LLM=false  # Force disable LLM (DiT-only mode)

Update & Maintenance

Script	Purpose
`check_update.bat`	Check and update from GitHub
`merge_config.bat`	Merge backed-up configurations after update
`install_uv.bat`	Install uv package manager
`quick_test.bat`	Test environment setup
`test_git_update.bat`	Test Git update functionality

Update workflow:

bash

check_update.bat          # 1. Check for updates (requires PortableGit/)
merge_config.bat          # 2. Merge settings back if conflicts occur

Portable Git Support

Place a PortableGit/ folder in your package to enable auto-updates:

batch

set CHECK_UPDATE=true     # in start_gradio_ui.bat or start_api_server.bat

Features: 10s timeout protection, smart conflict detection & backup, automatic rollback on failure, directory structure preserved in backups.

Environment Detection Priority

python_embedded\python.exe (if exists)
uv run acestep (if uv is installed)
Auto-install uv via winget or PowerShell

🍎 macOS Portable Package

For macOS users (Apple Silicon), we provide a portable package with pre-installed dependencies:

Download and extract: ACE-Step-1.5.zip
The package includes all dependencies pre-installed with MLX backend support
Requirements: Apple Silicon (M1/M2/M3/M4) with macOS

Quick Start Scripts

Script	Description
`start_gradio_ui_macos.sh`	Launch Gradio Web UI (MLX)
`start_api_server_macos.sh`	Launch REST API Server (MLX)

bash

# Make executable (first time only)
chmod +x start_gradio_ui_macos.sh start_api_server_macos.sh

# Launch Gradio Web UI with MLX backend
./start_gradio_ui_macos.sh

# Launch REST API Server with MLX backend
./start_api_server_macos.sh

The macOS scripts automatically set ACESTEP_LM_BACKEND=mlx and --backend mlx for native Apple Silicon acceleration.

Configuration

Configurable options are defined as variables at the top of each script. Open the script with a text editor to customize:

bash

# UI language (en, zh, he, ja)
LANGUAGE="en"

# Download source (auto, huggingface, modelscope)
DOWNLOAD_SOURCE="--download-source auto"

# Git update check (true/false)
CHECK_UPDATE="true"

# Model configuration
CONFIG_PATH="--config_path acestep-v15-turbo"
LM_MODEL_PATH="--lm_model_path acestep-5Hz-lm-1.7B"

AMD / ROCm GPUs

⚠️ uv run acestep installs CUDA PyTorch wheels and may overwrite an existing ROCm setup.

Windows - ROCm 7.2 (Python 3.12 Required)

Important: AMD ROCm 7.2 on Windows requires Python 3.12 (AMD officially provides Python 3.12 wheels only).

bash

# 1. Ensure you have Python 3.12 installed
python --version  # Should show Python 3.12.x

# 2. Create and activate a virtual environment
python -m venv venv_rocm
venv_rocm\Scripts\activate

# 3. Follow the installation steps in requirements-rocm.txt
# This installs ROCm SDK and PyTorch wheels from AMD's repository

# 4. Install dependencies
pip install -r requirements-rocm.txt

# 5. Launch with the ROCm-specific launcher
start_gradio_ui_rocm.bat
# OR
start_api_server_rocm.bat

See requirements-rocm.txt for detailed ROCm 7.2 installation steps.

Linux - ROCm 6.0+ (Python 3.11 or 3.12)

bash

# 1. Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate

# 2. Install ROCm-compatible PyTorch
pip install torch --index-url https://download.pytorch.org/whl/rocm6.0

# 3. Install ACE-Step
pip install -e .

# 4. Start the service
python -m acestep.acestep_v15_pipeline --port 7680

Note: torchcodec is not available for AMD ROCm GPUs due to CUDA-specific dependencies. ACE-Step automatically uses soundfile as a fallback for audio I/O, which provides full functionality on ROCm platforms.

GPU Detection Troubleshooting

If you see "No GPU detected, running on CPU" with an AMD GPU:

Run the diagnostic tool: python scripts/check_gpu.py
For RDNA3 GPUs, set HSA_OVERRIDE_GFX_VERSION:

GPU	Value
RX 7900 XT/XTX, RX 9070 XT	`export HSA_OVERRIDE_GFX_VERSION=11.0.0`
RX 7800 XT, RX 7700 XT	`export HSA_OVERRIDE_GFX_VERSION=11.0.1`
RX 7600	`export HSA_OVERRIDE_GFX_VERSION=11.0.2`

On Windows, use start_gradio_ui_rocm.bat / start_api_server_rocm.bat which set required environment variables automatically.
Verify ROCm installation: rocm-smi should list your GPU.

Linux (cachy-os / RDNA4)

See ACE-Step1.5-Rocm-Manual-Linux.md for a detailed ROCm manual tested with RDNA4 on cachy-os.

Intel GPUs

Item	Detail
Tested Device	Windows laptop with Ultra 9 285H integrated graphics
Offload	Disabled by default
Compile & Quantization	Enabled by default
LLM Inference	Supported (tested with `acestep-5Hz-lm-0.6B`)
nanovllm acceleration	NOT supported on Intel GPUs
Test Environment	PyTorch 2.8.0 from Intel Extension for PyTorch

Note: LLM inference speed may decrease when generating audio longer than 2 minutes. Intel discrete GPUs are expected to work but not yet tested.
Audio I/O: torchcodec is not available for Intel XPU GPUs. ACE-Step automatically uses soundfile as a fallback for audio I/O, which provides full functionality on Intel platforms.

CPU-Only Mode

ACE-Step can run on CPU for inference only, but performance will be significantly slower.

Training (including LoRA) on CPU is not recommended.
For low-VRAM systems, DiT-only mode (LLM disabled) is supported.

If you do not have a GPU, consider:

Using cloud GPU providers
Running inference-only workflows
Using DiT-only mode with ACESTEP_INIT_LLM=false

Linux Notes

Python 3.11 Pre-Release Issue

Some Linux distributions (including Ubuntu) ship Python 3.11.0rc1, which is a pre-release build. This can cause segmentation faults with the vLLM backend.

Recommendation: Use a stable Python release (≥ 3.11.12). On Ubuntu, install via the deadsnakes PPA.

If upgrading Python is not possible, use the PyTorch backend:

bash

uv run acestep --backend pt

Environment Variables (.env)

The .env file provides a centralized way to configure ACE-Step. Settings in .env are:

Used by Python scripts (CLI, API server, Gradio UI)
Now also used by launcher scripts (start_gradio_ui.bat, start_gradio_ui.sh, etc.)
Preserved across repository updates (unlike hardcoded values in launcher scripts)

bash

cp .env.example .env   # Copy and edit

Benefits of Using .env

✅ Survives Updates: Your custom model paths and settings won't be overwritten when you update ACE-Step
✅ Cross-Platform: Same configuration works on Windows, Linux, and macOS
✅ Version Control Safe: .env is in .gitignore, so your personal settings stay private

Key Variables

Variable	Values	Description
`ACESTEP_INIT_LLM`	`auto` / `true` / `false`	LLM initialization mode
`ACESTEP_CONFIG_PATH`	model name	DiT model path
`ACESTEP_LM_MODEL_PATH`	model name	LM model path
`ACESTEP_DOWNLOAD_SOURCE`	`auto` / `huggingface` / `modelscope`	Download source
`ACESTEP_API_KEY`	string	API authentication key
`PORT`	number	Server port (default: 7860)
`SERVER_NAME`	IP address	Server host (default: 127.0.0.1)
`LANGUAGE`	`en` / `zh` / `he` / `ja`	UI language (default: en)

LLM Initialization (`ACESTEP_INIT_LLM`)

Processing flow: GPU Detection → ACESTEP_INIT_LLM Override → Model Loading

Value	Behavior
`auto` (or empty)	Use GPU auto-detection result (recommended)
`true` / `1` / `yes`	Force enable LLM after GPU detection (may cause OOM)
`false` / `0` / `no`	Force disable for pure DiT mode

Example .env for different scenarios:

bash

# Auto mode (recommended)
ACESTEP_INIT_LLM=auto

# Force enable on low VRAM GPU
ACESTEP_INIT_LLM=true
ACESTEP_LM_MODEL_PATH=acestep-5Hz-lm-0.6B

# Force disable LLM for faster generation
ACESTEP_INIT_LLM=false

Command Line Options

Gradio UI (`acestep`)

Option	Default	Description
`--port`	7860	Server port
`--server-name`	127.0.0.1	Server address (use `0.0.0.0` for network access)
`--share`	false	Create public Gradio link
`--language`	en	UI language: `en`, `zh`, `he`, `ja`
`--batch_size`	None	Default batch size for generation (1 to GPU-dependent max). When not specified, defaults to `min(2, GPU_max)`
`--init_service`	false	Auto-initialize models on startup
`--init_llm`	auto	LLM init: `true` / `false` / omit for auto
`--config_path`	auto	DiT model (e.g., `acestep-v15-turbo`)
`--lm_model_path`	auto	LM model (e.g., `acestep-5Hz-lm-1.7B`)
`--offload_to_cpu`	auto	CPU offload (auto-enabled if VRAM < 20GB)
`--download-source`	auto	Model source: `auto` / `huggingface` / `modelscope`
`--enable-api`	false	Enable REST API alongside Gradio UI
`--api-key`	none	API key for authentication
`--auth-username`	none	Gradio authentication username
`--auth-password`	none	Gradio authentication password

Examples:

bash

# Public access with Chinese UI
uv run acestep --server-name 0.0.0.0 --share --language zh

# Pre-initialize models on startup
uv run acestep --init_service true --config_path acestep-v15-turbo

# Set default batch size to 4
uv run acestep --batch_size 4

# Enable API endpoints with authentication
uv run acestep --enable-api --api-key sk-your-secret-key --port 8001

# Use ModelScope as download source
uv run acestep --download-source modelscope

📥 Model Download

Models are automatically downloaded from HuggingFace or ModelScope on first run.

CLI Download

bash

uv run acestep-download                              # Download main model
uv run acestep-download --all                         # Download all models
uv run acestep-download --download-source modelscope  # From ModelScope
uv run acestep-download --model acestep-v15-sft       # Specific model
uv run acestep-download --list                        # List all available

Or with Python directly:

bash

python -m acestep.model_downloader                    # Download main model
python -m acestep.model_downloader --all              # Download all models

Manual Download (huggingface-cli)

bash

# Main model (vae, Qwen3-Embedding-0.6B, acestep-v15-turbo, acestep-5Hz-lm-1.7B)
huggingface-cli download ACE-Step/Ace-Step1.5 --local-dir ./checkpoints

# Optional LM models
huggingface-cli download ACE-Step/acestep-5Hz-lm-0.6B --local-dir ./checkpoints/acestep-5Hz-lm-0.6B
huggingface-cli download ACE-Step/acestep-5Hz-lm-4B --local-dir ./checkpoints/acestep-5Hz-lm-4B

# XL (4B) DiT models - requires ≥12GB VRAM (with offload)
huggingface-cli download ACE-Step/acestep-v15-xl-base --local-dir ./checkpoints/acestep-v15-xl-base
huggingface-cli download ACE-Step/acestep-v15-xl-sft --local-dir ./checkpoints/acestep-v15-xl-sft
huggingface-cli download ACE-Step/acestep-v15-xl-turbo --local-dir ./checkpoints/acestep-v15-xl-turbo

Shared Model Directory

If you have multiple ACE-Step installations (e.g., trainers, different versions), you can share a single model directory to avoid duplicate downloads and save disk space:

bash

# Add to your shell profile (~/.bashrc, ~/.zshrc, etc.)
export ACESTEP_CHECKPOINTS_DIR=~/ace-step-models

All installations will then use the same model files. You can also set this in your .env file.

Available Models

Model	Description	HuggingFace
Ace-Step1.5 (Main)	Core: vae, Qwen3-Embedding-0.6B, acestep-v15-turbo, acestep-5Hz-lm-1.7B	Link
acestep-5Hz-lm-0.6B	Lightweight LM (0.6B params)	Link
acestep-5Hz-lm-4B	Large LM (4B params)	Link
acestep-v15-base	Base DiT model	Link
acestep-v15-sft	SFT DiT model	Link
acestep-v15-turbo-shift1	Turbo DiT with shift1	Link
acestep-v15-turbo-shift3	Turbo DiT with shift3	Link
acestep-v15-turbo-continuous	Turbo DiT with continuous shift (1-5)	Link
acestep-v15-xl-base	XL (4B) Base DiT — higher quality, ≥12GB VRAM	Link
acestep-v15-xl-sft	XL (4B) SFT DiT — higher quality, ≥12GB VRAM	Link
acestep-v15-xl-turbo	XL (4B) Turbo DiT — higher quality, ≥12GB VRAM	Link

💡 Which Model Should I Choose?

ACE-Step automatically adapts to your GPU's VRAM. The UI pre-configures all settings (LM model, backend, offloading, quantization) based on your detected GPU tier:

Your GPU VRAM	Recommended DiT	Recommended LM Model	Backend	Notes
≤6GB	2B turbo	None (DiT only)	—	LM disabled; 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	`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

📖 For detailed GPU compatibility information (tier table, duration limits, batch sizes, adaptive UI defaults, memory optimization), see GPU Compatibility Guide.

Development

bash

# Add dependencies
uv add package-name
uv add --dev package-name

# Update all dependencies
uv sync --upgrade

ACE-Step 1.5 Installation Guide ​

Table of Contents ​

Requirements ​

Quick Start (All Platforms) ​

1. Install uv (Package Manager) ​

2. Clone & Install ​

3. Launch ​

🚀 Launch Scripts ​

Available Scripts ​

Windows ​

Linux ​

macOS (Apple Silicon / MLX) ​

Script Features ​

How to Modify Configuration ​

Update & Maintenance Tools ​

🪟 Windows Portable Package ​

Quick Start Scripts ​

Configuration ​

Update & Maintenance ​

Portable Git Support ​

Environment Detection Priority ​

🍎 macOS Portable Package ​

Quick Start Scripts ​

Configuration ​

AMD / ROCm GPUs ​

Windows - ROCm 7.2 (Python 3.12 Required) ​

Linux - ROCm 6.0+ (Python 3.11 or 3.12) ​

GPU Detection Troubleshooting ​

Linux (cachy-os / RDNA4) ​

Intel GPUs ​

CPU-Only Mode ​

Linux Notes ​

Python 3.11 Pre-Release Issue ​

Environment Variables (.env) ​

Benefits of Using .env ​

Key Variables ​

LLM Initialization (ACESTEP_INIT_LLM) ​

Command Line Options ​

Gradio UI (acestep) ​

📥 Model Download ​

CLI Download ​

Manual Download (huggingface-cli) ​

Shared Model Directory ​

Available Models ​

💡 Which Model Should I Choose? ​

Development ​

ACE-Step 1.5 Installation Guide

Table of Contents

Requirements

Quick Start (All Platforms)

1. Install uv (Package Manager)

2. Clone & Install

3. Launch

🚀 Launch Scripts

Available Scripts

Windows

Linux

macOS (Apple Silicon / MLX)

Script Features

How to Modify Configuration

Update & Maintenance Tools

🪟 Windows Portable Package

Quick Start Scripts

Configuration

Update & Maintenance

Portable Git Support

Environment Detection Priority

🍎 macOS Portable Package

Quick Start Scripts

Configuration

AMD / ROCm GPUs

Windows - ROCm 7.2 (Python 3.12 Required)

Linux - ROCm 6.0+ (Python 3.11 or 3.12)

GPU Detection Troubleshooting

Linux (cachy-os / RDNA4)

Intel GPUs

CPU-Only Mode

Linux Notes

Python 3.11 Pre-Release Issue

Environment Variables (.env)

Benefits of Using .env

Key Variables

LLM Initialization (`ACESTEP_INIT_LLM`)

Command Line Options

Gradio UI (`acestep`)

📥 Model Download

CLI Download

Manual Download (huggingface-cli)

Shared Model Directory

Available Models

💡 Which Model Should I Choose?

Development