therealcyberlord / dcgan-abstract-art

Using DCGAN to generate images of abstract art, with the option to upscale with ESRGAN.

Generating Abstract Art Live Demo

The goal of this project is to train a DCGAN to generate new works of abstract art. The implementation will be done in PyTorch.

This project will utilze the model proposed by the 2015 paper "UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS", which advocates the idea of using strided convolution to replace pooling layers.

Dataset used: https://www.kaggle.com/datasets/bryanb/abstract-art-gallery

Web App

Huggingface Demo: https://huggingface.co/spaces/therealcyberlord/abstract-art-generation

Running the project locally

Download the libraries used with

pip install -r requirements.txt

• Update: ESRGAN model exceeds the github-LF size, please download it from my huggingface model page: https://huggingface.co/therealcyberlord/ESRGAN-Abstract-Art/tree/main
  download the huggingface repo and paste esrgan.pt into the Checkpoints folder otherwise the demo will not work properly

Navigate to the cloned/downloaded directory (DCGAN-Abstract-Art) and run python Main.py [num_images] [--seed=somenumber] [--checkpoint=somenumber] [--srgan]

eg: python Main.py 6 --seed 90 --checkpoint 150 --srgan

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the arg values will be in type integer and num_images must be in the range (0, 120], only the argument num_images is required. Try reducing the number of images that you are applying super resolution if you are running out of memory.

checkpoint_num: default: 150, you can also use your own trained checkpoints, see the ipynb notebook to continue training.

Run python Main.py -h for more information

Upscaled with ESRGAN

Sources:

• DCGAN Arxiv Paper: https://arxiv.org/pdf/1511.06434v2.pdf
• ESRGAN Arvix Paper: https://arxiv.org/pdf/1809.00219.pdf
• PyTorch GANS: https://github.com/eriklindernoren/PyTorch-GAN
• This also take a lot of inspiration from the PyTorch DCGAN Tutorial, check it out here