The purpose of this project is to expand knowledge of traditional GAN architectures by exploring NVIDIA's Progressive GAN research. Progressive Growing of GANs for Improved Quality, Stability, and Variation

• pip install -r requirements.txt - from project root

• python utils.py init - download all logs for exploration then init empty models and results directory
• You must REMOVE the logs folder this command will NOT overwrite an log files.

• python utils.py download - download the pre-trained model files and image sets.
• Options = [cars, cyber, dogs, faces, potatoes]; default = faces
• This command will NOT override existing models you must delete both imgs and models folders to download a new image and model set

• tensorboard --logdir logs - to start the tensorboard and explore the current models training progress.

• WARNING! This can overwrite existing models!
• python train.py - continue training or re-train model.
• Changing the LOAD_MODEL global in config.py; chooses between re-training (False) or continuing training (True) for the model.

• python utils.py sample - generates sample images, by default this will generate 10 images at 64x64.
• This command can be customized to generate a chosen amount of images at a chosen size python utils.py sample <num_images> <size_factor>
• Size factors: 0 = 4x4, 1 = 8x8, 2 = 16x16, 3 = 32x32, 4 = 64x64, 5 = 128x128, 6 = 256x256, 7 = 512x512, 8 = 1024x1024

• python utils.py transform - generates output images, by default this will output all batches at 128x128.
• This command can be customized to generate a chosen amount of batches python utils.py transform <num_batches>

• python utils.py removedups - remove duplicate images in the imgs folder using an image hash function.
• This utility is designed to for cleaning custom image datasets of duplicate images
• The hash_size for this function can be customized to have a looser hash criteria i.e accepting a larger difference between "duplicates"
• This function will output how many duplicates have been removed and a path list for all duplicate images. NOTE: paths are not paired with duplicates.

@misc{https://doi.org/10.48550/arxiv.1701.07875,
  doi = {10.48550/ARXIV.1701.07875},
  url = {https://arxiv.org/abs/1701.07875},
  author = {Arjovsky, Martin and Chintala, Soumith and Bottou, Léon},
  keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Wasserstein GAN},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{https://doi.org/10.48550/arxiv.1704.00028,
  doi = {10.48550/ARXIV.1704.00028},
  url = {https://arxiv.org/abs/1704.00028},
  author = {Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron},
  keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Improved Training of Wasserstein GANs},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{https://doi.org/10.48550/arxiv.1710.10196,
  doi = {10.48550/ARXIV.1710.10196},
  url = {https://arxiv.org/abs/1710.10196},
  author = {Karras, Tero and Aila, Timo and Laine, Samuli and Lehtinen, Jaakko},
  keywords = {Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}