Metadata-Version: 2.1
Name: Artist-Engineering-Geek
Version: 0.0.5
Summary: A bunch of GANs and data downloaders to make a custom AI artist
Home-page: https://github.com/Fatima-x-Nikhil/Artist
Author: Nikhil Melgiri
Author-email: nmelgiri@uwaterloo.ca
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/Fatima-x-Nikhil/Artist/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# Artist
## Motivation
- An easy to edit codebase for Progressive GAN originally published by this [research paper][Progressive GAN Research Paper] and other GANs
- Supplement my personal projects
- Resume builder
- For fun and to understand state-of-the-art AI

## Installation
#### CUDA Installation
Ensure you have a GPU if you want to train in any reasonable amount of time.
- [Install CUDA here][CUDA Install]
- [Install cuDNN here][cuDNN Install]
#### Project Installation
```sh
git clone https://github.com/Fatima-x-Nikhil/Artist.git
cd Artist
pip install -r requirements.txt
cd src/models
pip install -e .
```

## Running the program
To train the program, run the "Train.ipynb" notebook and alter your parameters at will.
There should be sufficient in-code documentation for you to understand what the hell is going on

   [CUDA Install]: <https://developer.nvidia.com/cuda-downloads>
   [cuDNN Install]: <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>
   [Progressive GAN Research Paper]: <https://arxiv.org/abs/1710.10196>

