A Large Language Model–Powered Interactive Canvas for Generative Artists

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Generative artists work in code. Using programming languages like Processing or AI text-to-image tools, they translate expressive semantics into lines of code that form swirling, colorful patterns or surrealistic landscapes. 

But coding art is a time-consuming, complicated process. While a pencil’s eraser might fix an errant line or a little yellow might brighten a painting’s dark skyline, improving generative art takes trial and error through numerous iterations with often frustratingly opaque interfaces.

After interviewing expert digital artists on these creative frustrations, Stanford scholars have developed a tool called Spellburst to improve the ideation and editing process.

“Translating an artist’s imagination into code takes a lot of time, and it’s very difficult,” says Hariharan Subramonyam, assistant professor at the Graduate School of Education and a faculty fellow at the Stanford Institute for Human-Centered AI. “A large language model can give you a good starting point. But when the artist wants to explore different textures, different colors or…

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