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Generative Art Cycle

Sweaty Machine Learning

My mind constantly orients itself to make any necessary task a more creative one, but despite my best efforts it’s still hard to design while riding a bicycle. While the past 16 months and thousands of cycled kilometres have been the best way to clear my head and keep fit, I found myself dreaming up ways to disrupt a circular task.

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Inspired by friends generating intriguing images using AI engines, I collected stats from my Wahoo bike trainer and Zwift virtual cycling app, and transformed them into Python Script parameters for VQGAN+CLIP, running on Google Collab’s virtual processors.

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I first translated my cycling data into a series of prompts to guide AI creation; for example, if I cycled a distance of 25.3 km, I’d have the AI complete 253,000 iterations of the assigned prompt. I interpreted additional factors like average speed, watts generated, ride duration, vertical climb, and distance. The visual training information is input from the scenic details of Zwift's virtual world and real photography of the mapped locations.

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Each goal yields unique results, and generates a trippy, abstracted representation of my accomplishments, like cycling a virtual Tour de France or climbing the equivalent of Everest in vertical metres. Using this goal-driven art generated in achieving these goals felt natural to craft custom cycling attire and accessories that reflected what I did to get there.

For me, cycling continues to be the best way to organize my thoughts, and collaborating is the best way to push beyond my comfort and perspective. I didn’t expect machine learning to become my latest accomplice, but with every ride offering a chance to generate a new piece of art, I now have even more motivation to climb that next hill.

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