This project aims to generate anime faces using Generative Adversarial Networks (GANs). We have implemented and trained a generator and a discriminator model to achieve this task.
- Clone the repository:
git clone https://github.com/PJF9/Animefaces-GAN.git)
cd AnimeFaces-GAN- Create a virtual environment and activate it:
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`- Install the dependencies:
pip install -r requirements.txt-
Add
kaggle.jsonfile to /home/usr/.kaggle (you can generate a key here.) -
Modify
src/config.pyas needed to adjust the settings as you prefer.
- To downlaod the dataset, run:
python3 data.py- To train the GAN models, run:
python3 train.py- To make a single prediction, run:
python3 predict.pygenerator.py: Defines the generator model architecture.discriminator.py: Defines the discriminator model architecture.
trainer.py: Contains the training loop for the GAN models.
data.py: Handles data loading and preprocessing.device.py: Manages device configuration (CPU/GPU).log.py: Contains logging functionalities.save.py: Functions for saving models and outputsvisualization.py: Functions for visualizing generated images.config.py: Handles configuration file parsing.
data.py: Script to preprocess the dataset.train.py: Script to start training the GAN models.predict.py: Script to generate images using the trained generator model.
While running the scripts, some extra directories will be created:
./checkpoints(config.MODELS_PATH): This directory will save all the checkpoints of training and the best generator model../plots(config.PLOTS_PATH): This directory will save the loss curves after training../generated(config.IMAGES_PATH): This directory will save the generated images during training and the results from the predict.py script.
Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.