Generative Art Using Neural Visual Grammars and Dual Encoders
Arnheim 1
The original algorithm from the paper Generative Art Using Neural Visual Grammars and Dual Encoders running on 1 GPU allows optimization of any image using a genetic algorithm. This is much more general but much slower than using Arnheim 2 which uses gradients.
Arnheim 2
A reimplementation of the Arnheim 1 generative architecture in the CLIPDraw framework allowing optimization of its parameters using gradients. Much more efficient than Arnheim 1 above but requires differentiating through the image itself.
Usage
Usage instructions are included in the Colabs which open and run on the free-to-use Google Colab platform - just click the buttons below! Improved performance and longer timeouts are available with Colab Pro.
Citing this work
If you use this code (or any derived code), data or these models in your work, please cite the relevant accompanying paper.
@misc{fernando2021genart,
title={Generative Art Using Neural Visual Grammars and Dual Encoders},
author={Chrisantha Fernando and S. M. Ali Eslami and Jean-Baptiste Alayrac and Piotr Mirowski and Dylan Banarse and Simon Osindero}
year={2021},
eprint={2105.00162},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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