originally posted january 2026 | updated june 2026
so we all know and love old artbreeder with its weird indeterminate images. but the freaks who know machine learning want to put those days behind in exchange for stuff like txt2img and high res stuff. to each their own, it's pretty cool that we can do all that shit, but what really pisses me off is that no one writing about how to generate images locally seems to give a shit about the crusty 256px images of the distant early 2020s.
for the record, the model being used here, biggan, was trained on imagenet, which was scraped from image searches for nouns on wordnet. it's overall a different beast than the models that are more popular now like stable diffusion.
well, so what is this? this is my guide on how to run artbreeder's open source ancestor, ganbreeder, on your own silicon. it's not quite identical to legacy artbreeder, and you can't pick or manipulate genes directly, but you can breed images just fine, and should be able to do so indefinitely and entirely offline. nothing will change without your direct intervention. it's secret horse world forever (sort of; neuralblender used a different model).
who is this for? well, you need pretty average hardware — i'm using an acer aspire 5, which is a circa 2020 laptop with an 11th gen i5 cpu and an nvidia mx350 gpu that i'm pretty sure doesn't even get poked. hell, this ran just fine on my 2013 thinkpad t440, generating a batch in around 40 seconds.
as of june 2026, there are two ways to do this: the legacy tutorial covers my original fork from january 2026 (which is a rather manual process), while the self-contained version is a single executable of that fork put together by my dear friend N—, who actually knows computers professionally. the self-contained version is highly recommended (both for use of use and one new functionality), but i suggest reading the original tutorial for an understanding of the behind-the-scenes.