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.
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.
A spatial transformer-based Arnheim implementation for generating collage images. It employs a combination of evolution and training to create collages from opaque to transparent image patches. Example patch datasets are provided under CC BY-SA 4.0 licence. The 'Fruit and veg' patches are based on a subset of the Kaggle Fruits 360 under the same license.
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.
Arnheim 1
Arnheim 2
Arnheim 3
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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CLIPDraw provided under license, Copyright 2021 Kevin Frans.
Other works may be copyright of the authors of such work.