README.md

metadata

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:

   git clone <your-repo-url>
   cd comfyui-ipAdapter-space
   pip install -r requirements.txt
   

2. Run the Application:

   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

# 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.