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.

Arnheim 1

Arnheim 2

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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