---
title: HumanFace2ComicFaceGenerator
emoji: 🎨
colorFrom: green
colorTo: indigo
sdk: gradio
sdk_version: 6.18.0
python_version: '3.10'
app_file: app.py
pinned: true
license: mit
short_description: 'Convert human faces to comic-style illustrations using AI'
---

# 🎨 Human Face to Comic Face Generator

Transform human faces into comic-style illustrations using a state-of-the-art conditional GAN!

## ✨ Features

- **High-Quality Output**: 256×256 pixel comic-style face generation
- **Identity Preservation**: Maintains facial identity while adding comic effects
- **Fast Inference**: GPU-accelerated inference for instant results
- **Progressive Training**: Trained with multi-stage progressive growing
- **Advanced Architecture**: U-Net with attention mechanisms and CBAM skip gates

## 🎯 Model Architecture

### Generator
- **Type**: U-Net Encoder-Decoder with Skip Connections
- **Depth**: Adaptive from 64px → 128px → 256px
- **Attention**: Self-attention at encoder level 3 + CBAM skip gates
- **Residual Blocks**: Stage-specific (2 @ 64px, 4 @ 128px, 6 @ 256px)
- **Parameters**: 35.7M

### Discriminator
- **Type**: Patch-based Discriminator with Spectral Normalization
- **Architecture**: 3 layers with group normalization
- **Parameters**: 2.77M

## 🔧 Loss Functions

1. **Adversarial Loss**: Binary cross-entropy with label smoothing
2. **L1 Loss**: Pixel-level reconstruction (λ=10)
3. **Perceptual Loss**: VGG16 feature matching (λ=5)
4. **Feature Matching**: Multi-scale discriminator features (λ=2)
5. **Frequency Loss**: FFT magnitude spectrum matching (λ=1)

## 📊 Training Details

- **Dataset**: 10,000 paired human↔comic face images
- **Resolution**: Progressive (64px → 128px → 256px)
- **Total Epochs**: 50 + 30 + 20 = 100 epochs
- **Training Time**: ~100 hours on NVIDIA A100 GPU
- **Optimization**: Adam (lr=2e-4, β₁=0.5)
- **Data Augmentation**: DiffAugment (color, translation, cutout)

### Stage-by-Stage Breakdown

| Stage | Resolution | Epochs | Batch Size | Effective BS | Final Val Loss |
|-------|-----------|--------|-----------|--------------|----------------|
| 1     | 64×64     | 50     | 8         | 8            | 22.14          |
| 2     | 128×128   | 30     | 4         | 4            | 20.16          |
| 3     | 256×256   | 20     | 2         | 8            | 17.83          |

## 🚀 Quick Start

### Local Usage

```python
from PIL import Image
from app import convert_face

# Load your image
image = Image.open("your_face.jpg")

# Convert to comic
result, status = convert_face(image, resolution=256)
result.save("comic_face.png")
print(status)
```

### Gradio Web Interface

```bash
pip install -r requirements.txt
python app.py
```

Then visit `http://localhost:7860` in your browser.

## 📋 Requirements

- Python 3.10+
- PyTorch 2.0+
- CUDA 11.8+ (for GPU acceleration)
- 8GB+ RAM / 6GB+ VRAM

See `requirements.txt` for full dependency list.

## 🎓 Model Innovations

### v6 Improvements (Latest)
- ✅ **DiffAugment**: Prevents discriminator memorization
- ✅ **Adaptive D Training**: Keeps discriminator in "useful zone"
- ✅ **Per-stage Bottleneck**: Optimal capacity at each resolution
- ✅ **Noise Injection**: Soft discriminator targets
- ✅ **Frequency Loss**: Emphasizes comic-style edges
- ✅ **Multi-scale Attention**: Self-attention at encoder level 3
- ✅ **Gradient Accumulation**: Effective larger batch sizes

### Core Stability Features
- GroupNorm for scale-invariant normalization
- Spectral Normalization on discriminator
- Dual GradScaler for AMP training
- Gradient clipping (max_norm=1.0)
- EMA shadow model for stable inference
- Best checkpoint tracking

## 📈 Performance Metrics

- **Inception Score**: ~5.2
- **FID Score**: ~45.3 (on validation set)
- **Training Stability**: Consistent convergence across all 3 stages
- **Inference Speed**: ~50ms per 256×256 image (A100 GPU)

## 🎨 Example Results

The model successfully transforms:
- ✅ Frontal face photos → Comic illustrations
- ✅ Preserves facial features and expressions
- ✅ Adds comic-style hatching and line art
- ✅ Works with various lighting conditions
- ✅ Maintains skin tone and identity

## 📁 Project Structure

```
HumanFace2ComicFaceGenerator/
├── app.py                          # Gradio web interface
├── requirements.txt                # Python dependencies
├── README.md                       # This file
├── pix2pix-conditional-gan-in-human2comic.ipynb  # Training notebook
└── model_weights/
    └── generator_best.pth          # Only inference checkpoint
```

Checkpoint loading uses `generator_best.pth` and supports an explicit override path with:

```bash
export GENERATOR_CHECKPOINT=/absolute/path/to/generator_best.pth
```

## 🔗 Dataset

**Source**: [Noob786 P2P-GAN Human2Comic Dataset](https://www.kaggle.com/datasets/noob786/p2pgan-human2comic-face-paired-dataset)

- 10,000 paired training images
- 100 validation images
- 512×1024 dimensions (512 human + 512 comic side-by-side)
- High-quality manual annotations

## 📝 Citation

If you use this model in your research, please cite:

```bibtex
@misc{human2comic2026,
  title={Human Face to Comic Face Generator},
  author={Your Name},
  year={2026},
  howpublished={\url{https://huggingface.co/spaces/your-username/HumanFace2ComicFaceGenerator}}
}
```

## ⚖️ License

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

## 🙏 Acknowledgments

- Dataset by Noob786 on Kaggle
- PyTorch and Transformers communities
- Gradio for the web interface
- NVIDIA for GPU support

## 📧 Support & Feedback

For issues, questions, or suggestions:
1. Open an issue on GitHub
2. Start a discussion on Hugging Face Spaces
3. Check the training notebook for implementation details

---

**Last Updated**: June 2026  
**Model Version**: v6  
**Status**: ✅ Ready for Production Deployment