README.md

metadata

title: CycleGAN Face-Sketch Converter
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.28.0
app_file: app.py
pinned: false

🎨 CycleGAN Face-Sketch Converter

Convert face photos to sketches and vice versa using CycleGAN.

🚀 Deployment on Hugging Face Spaces

Step 1: Prepare Your Models

After training, run this cell in your notebook:

# Run this in your training notebook
export_for_huggingface(checkpoint_epoch=100)  # Replace 100 with your best epoch

This creates two files in deployment_models/:

• photo_to_sketch.pth (~44 MB)
• sketch_to_photo.pth (~44 MB)

Step 2: Create Hugging Face Space

1. Go to Hugging Face Spaces
2. Click "Create new Space"
3. Configure:
   • Space name: cyclegan-face-sketch
   • License: Choose appropriate license
   • Select SDK: Streamlit
   • Space hardware: CPU Basic (free) or GPU for faster inference

Step 3: Upload Files

Upload these files to your Space:

your-space/
├── app.py                    # Streamlit application
├── requirements.txt          # Python dependencies
├── photo_to_sketch.pth       # Model file (Photo → Sketch)
├── sketch_to_photo.pth       # Model file (Sketch → Photo)
└── README.md                 # This file (optional)

Step 4: Your Space will Auto-Build

Hugging Face will automatically:

1. Install dependencies from requirements.txt
2. Run your Streamlit app
3. Provide a public URL

📦 File Sizes

• photo_to_sketch.pth: ~44 MB
• sketch_to_photo.pth: ~44 MB
• Total: ~88 MB (well within free tier limits)

🎯 Features

• ✅ Photo to Sketch conversion
• ✅ Sketch to Photo conversion
• ✅ Auto-detection of input type
• ✅ Camera input support
• ✅ Download results
• ✅ Responsive UI

💡 Usage Tips

For Best Results:

• Use clear, front-facing photos
• Ensure good lighting
• Images are automatically resized to 256x256

Hardware:

• CPU Basic (Free): ~3-5 seconds per image
• GPU T4 (Paid): ~0.5 seconds per image

🔧 Local Testing

Before deploying, test locally:

# Install dependencies
pip install -r requirements.txt

# Run Streamlit app
streamlit run app.py

Then open http://localhost:8501

📊 Model Details

• Architecture: CycleGAN with ResNet-based generators
• Input Size: 256x256 RGB images
• Training: Unpaired face-sketch dataset
• Loss Functions:
  • Adversarial loss (LSGAN)
  • Cycle consistency loss
  • Identity loss

🐛 Troubleshooting

Space doesn't start:

• Check all files are uploaded correctly
• Verify file names match exactly in app.py
• Check Space logs for errors

Out of memory:

• Use CPU Basic hardware
• Models are optimized for CPU inference

Slow inference:

• Upgrade to GPU hardware in Space settings
• Or use batch processing

📝 License

[Your License Here]

🙏 Acknowledgments

• CycleGAN paper: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Person Face Sketches dataset: Kaggle