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| 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: | |
| ```python | |
| # 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](https://huggingface.co/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: | |
| ```bash | |
| # 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](https://arxiv.org/abs/1703.10593) | |
| - Person Face Sketches dataset: [Kaggle](https://www.kaggle.com/datasets/almightyj/person-face-sketches) |