DurtyDhiana/ComfyUI-Style-IPAdapterGenerator

🎨 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. 1.Clone and Setup:

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

1. 1.Run the Application:

   python app.py

1. 1.Access the Interface: Open your browser to http://localhost:7860

Hugging Face Space Deployment

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

📖 Usage Guide

Basic Workflow

1. 1.Select Model: Choose between Stable Diffusion 1.5 or SDXL
2. 2.Enter Prompt: Describe the image you want to generate
3. 3.Upload Reference: Provide a reference image (face, style, or composition guide)
4. 4.Adjust Settings: Fine-tune generation parameters
5. 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. 1.Fork the repository
2. 2.Create a feature branch
3. 3.Make your changes
4. 4.Test thoroughly
5. 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.