# CycleGAN Image Style Transfer (Horse to Zebra)

This project implements an end-to-end CycleGAN model for unpaired image style transfer, specifically focused on the **Horse to Zebra** dataset.

## Project Structure
### TensorFlow Version (Recommended for this system)
- `tf_dataset.py`: TensorFlow Data loader.
- `tf_models.py`: Keras/TF CycleGAN architectures.
- `tf_train.py`: TensorFlow training script.
- `tf_predict.py`: TensorFlow inference script.

### PyTorch Version
- `dataset.py`: PyTorch Dataset class.
- `models.py`: PyTorch Generator and Discriminator.
- `train.py`: PyTorch training script.
- `predict.py`: PyTorch inference script.

- `download_data.py`: Script to download and extract the Horse2Zebra dataset.
- `requirements.txt`: Project dependencies.

## Setup
1. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
2. Download the dataset:
   ```bash
   python download_data.py
   ```

## Training
### TensorFlow
```bash
python tf_train.py
```

### PyTorch
```bash
python train.py
```
Checkpoints and sample results will be saved in the `saved_images/` directory or as `.h5` files.

## Inference
### TensorFlow
```bash
python tf_predict.py
```

### PyTorch
```bash
python predict.py
```
The result will be saved as `tf_prediction.png` or `prediction_result.png`.

## Web Application
A premium web interface is included for easy interaction with the models.

### Features
- **Bidirectional Style Transfer**: Switch between Horse ➔ Zebra and Zebra ➔ Horse.
- **Glassmorphic UI**: Modern, responsive design with smooth animations.
- **Real-time Preview**: See your uploaded image and stylized result side-by-side.
- **One-click Download**: Save your stylized art instantly.

### Running the App
1. Start the Flask server:
   ```bash
   python app.py
   ```
2. Open your browser and go to `http://localhost:5000`.

## Notes
- The model uses **PatchGAN** for the discriminator and a **ResNet-based generator** with 9 residual blocks for 256x256 images.
- Training is optimized for both GPU and CPU.
- The identity loss is currently set to 0 to speed up training, but can be adjusted in the training scripts (LAMBDA_IDENTITY or through `identity_loss`).

## Troubleshooting
- **PyTorch DLL Error (WinError 1114)**: If you encounter this on Windows, it is often related to GPU driver conflicts or power management. It is recommended to use the **TensorFlow version** provided in this repository as it is confirmed to be stable in this environment.