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I've been digging into StyleSynthesis lately, and I'm finding the structure of the style descriptors notably interesting.It truly seems like there's a subtle art to balancing the high-level aesthetic goals with the low-level parameter tweaking that actually makes the style work. The way different aspects like color palettes, texture properties, and even abstract concepts like "modernity" get codified into these descriptors is pretty impressive, and can affect a lot.
I'm curious how others are approaching the creation and customization of these descriptors. Are you focusing on manually adjusting parameters based on understanding the underlying generative model, or are you relying more on pre-built descriptors and trying to blend them together? Has anyone experimented with using machine learning techniques to automatically generate or optimize style descriptors based on a large dataset of visual examples? I’d love to hear your experiences to learn more about what works and what pitfalls to watch out for when wrangling those style descriptors.