---
title: ComfyUI-Style IPAdapter Generator
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.39.0
app_file: app.py
pinned: false
license: mit
---

# 🎨 ComfyUI-Style IPAdapter Generator

A Hugging Face Space that replicates core ComfyUI + IPAdapter functionality using Gradio. Generate images using text prompts and reference images with advanced AI models.

## ✨ Features

- **Text-to-Image Generation**: Create images from detailed text descriptions
- **IPAdapter Integration**: Use reference images to guide generation (faces, styles, compositions)
- **Multiple Models**: Support for Stable Diffusion 1.5 and SDXL
- **Advanced Controls**: Fine-tune generation with guidance scale, steps, and resolution
- **Face Enhancement**: Optional CodeFormer/GFPGAN integration for face improvement
- **LoRA Support**: Apply custom style models for unique aesthetics
- **Side-by-Side Comparison**: View reference and generated images together
- **Memory Optimized**: Works on both CPU and GPU with automatic fallbacks

## 🚀 Quick Start

### Local Installation

1. **Clone and Setup**:
   ```bash
   git clone <your-repo-url>
   cd comfyui-ipAdapter-space
   pip install -r requirements.txt
   ```

2. **Run the Application**:
   ```bash
   python app.py
   ```

3. **Access the Interface**:
   Open your browser to `http://localhost:7860`

### Hugging Face Space Deployment

1. **Create a new Space** on Hugging Face
2. **Upload files**: `app.py`, `requirements.txt`, `README.md`
3. **Select hardware**: CPU (free) or GPU (paid) based on your needs
4. **Deploy**: The space will automatically build and launch

## 📖 Usage Guide

### Basic Workflow

1. **Select Model**: Choose between Stable Diffusion 1.5 or SDXL
2. **Enter Prompt**: Describe the image you want to generate
3. **Upload Reference**: Provide a reference image (face, style, or composition guide)
4. **Adjust Settings**: Fine-tune generation parameters
5. **Generate**: Click the generate button and wait for results

### Parameters Explained

#### Core Settings
- **Text Prompt**: Detailed description of desired image
- **Reference Image**: Guide image for IPAdapter (faces work best)
- **Model**: Base diffusion model (SD 1.5 for speed, SDXL for quality)

#### Generation Controls
- **Guidance Scale** (1-20): How closely to follow the prompt (7.5 recommended)
- **IPAdapter Scale** (0-2): Strength of reference image influence (1.0 recommended)
- **Resolution**: Output image dimensions (512x512 for speed, higher for quality)
- **Inference Steps** (10-50): Quality vs speed tradeoff (20 recommended)
- **Seed**: For reproducible results (0 for random)

#### Enhancement Options
- **Face Enhancement**: Improve facial details in generated images
- **CodeFormer vs GFPGAN**: Different face enhancement algorithms
- **LoRA Path**: Local path to custom style models
- **LoRA Scale**: Strength of style model application

### Best Practices

#### For Face Generation
- Use clear, well-lit reference photos
- Keep IPAdapter scale between 0.8-1.2
- Enable face enhancement for better results
- Use descriptive prompts: "professional headshot, studio lighting"

#### For Style Transfer
- Use artistic references (paintings, illustrations)
- Adjust IPAdapter scale based on desired style strength
- Experiment with different guidance scales
- Consider using LoRA models for consistent styles

#### Performance Optimization
- Use 512x512 resolution for faster generation
- Reduce inference steps to 15-20 for speed
- Enable face enhancement only when needed
- Use CPU mode if GPU memory is limited

## 🛠️ Technical Details

### Architecture
- **Frontend**: Gradio web interface
- **Backend**: Hugging Face Diffusers + IPAdapter
- **Models**: Stable Diffusion 1.5/XL with IPAdapter weights
- **Enhancement**: CodeFormer/GFPGAN for face improvement
- **Styling**: LoRA support for custom aesthetics

### Memory Management
- Automatic model loading/unloading
- GPU memory optimization with xformers
- CPU fallback for limited hardware
- Efficient attention mechanisms

### Supported Formats
- **Input Images**: JPG, PNG, WebP
- **Output**: PNG format
- **LoRA Models**: .safetensors, .ckpt files

## 🔧 Configuration

### Environment Variables
```bash
# Optional: Set device preference
CUDA_VISIBLE_DEVICES=0

# Optional: Set cache directory
HF_HOME=/path/to/cache
```

### Hardware Requirements

#### Minimum (CPU)
- 8GB RAM
- 10GB storage
- Generation time: 2-5 minutes

#### Recommended (GPU)
- NVIDIA GPU with 6GB+ VRAM
- 16GB RAM
- 20GB storage
- Generation time: 10-30 seconds

## 📝 Example Prompts

### Portrait Generation
```
"A professional headshot photo of a person, studio lighting, high quality, detailed facial features"
```

### Artistic Styles
```
"An oil painting portrait in the style of Renaissance masters, dramatic lighting, classical composition"
```

### Fantasy/Sci-Fi
```
"A cyberpunk character with neon lighting, futuristic elements, digital art style"
```

### Anime/Illustration
```
"An anime-style character portrait, vibrant colors, detailed eyes, manga illustration"
```

## 🐛 Troubleshooting

### Common Issues

**Model Loading Errors**
- Check internet connection for model downloads
- Ensure sufficient disk space (20GB+)
- Try switching to CPU mode if GPU memory insufficient

**Generation Failures**
- Verify reference image is valid (JPG/PNG)
- Check prompt length (keep under 200 characters)
- Reduce resolution if memory errors occur

**Slow Performance**
- Use smaller resolutions (512x512)
- Reduce inference steps
- Disable face enhancement
- Switch to CPU mode if GPU is overloaded

**Face Enhancement Issues**
- Ensure face is clearly visible in reference
- Try different enhancement algorithms
- Adjust IPAdapter scale for better face preservation

## 🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

## 📄 License

This project is licensed under the MIT License. See LICENSE file for details.

## 🙏 Acknowledgments

- Hugging Face for the Diffusers library and model hosting
- IPAdapter team for the reference image integration
- ComfyUI for inspiration and workflow concepts
- Gradio team for the excellent web interface framework

## 📞 Support

- **Issues**: Report bugs via GitHub Issues
- **Discussions**: Join the community discussions
- **Documentation**: Check the Hugging Face Spaces documentation

---

**Note**: This is an educational project replicating ComfyUI functionality. For production use, consider the original ComfyUI or commercial alternatives.