Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,407 Bytes
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# ACE-Step 1.5 Custom Edition - Quick Start Guide
## Installation
### Option 1: Local Setup
1. **Clone the repository**
```bash
git clone https://github.com/yourusername/ace-step-custom.git
cd ace-step-custom
```
2. **Create virtual environment**
```bash
python -m venv venv
# On Windows:
venv\Scripts\activate
# On Linux/Mac:
source venv/bin/activate
```
3. **Run setup**
```bash
python scripts/setup.py
```
4. **Download model**
```bash
python scripts/download_model.py
```
5. **Launch application**
```bash
python app.py
```
6. **Open browser to** `http://localhost:7860`
### Option 2: HuggingFace Spaces
1. Create new Space on HuggingFace
2. Upload all project files
3. Set Space configuration:
- SDK: `gradio`
- Python: `3.10`
- GPU: `A10G` (or better)
4. Space will auto-deploy
## Usage
### Tab 1: Standard ACE-Step
Standard interface with all original ACE-Step features:
- Text-to-music generation
- Variation generation
- Repainting sections
- Lyric editing
### Tab 2: Timeline Workflow
Advanced timeline-based generation:
1. Enter prompt and lyrics
2. Set context length (0-120s)
3. Click "Generate" for 32s clips
4. Clips auto-blend into timeline
5. Use "Extend" to continue
6. Use "Inpaint" to edit regions
### Tab 3: LoRA Training
Train custom models:
1. Upload audio files (10+ recommended)
2. Set training parameters
3. Click "Start Training"
4. Download trained model
5. Use in Tab 1 or Tab 2
## Tips
- **First time:** Start with Standard tab to understand basics
- **For longer songs:** Use Timeline tab with context length 30-60s
- **For custom styles:** Train LoRA with 20+ similar audio files
- **GPU recommended:** 8GB+ VRAM for best performance
- **CPU mode:** Works but slower, use shorter durations
## Troubleshooting
### Out of Memory
- Reduce batch size in LoRA training
- Use shorter audio durations
- Close other GPU applications
### Poor Quality
- Increase context length
- Try different seeds
- Adjust temperature (0.6-0.8 is usually good)
### Blend Artifacts
- Reduce lead-in/lead-out durations
- Ensure consistent style across clips
- Use lower context length for more variety
## Support
- GitHub Issues: [Report bugs here]
- Documentation: See `docs/` directory
- Examples: See `examples/` directory
## Credits
Based on ACE-Step by ACE Studio and Step Fun
- Website: https://ace-step.github.io/
- Paper: https://arxiv.org/abs/2506.00045
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