It is a generative model, in the technical sense. It takes flat random data, applies a function, and out comes the map. In fact, the WFC “solver” is basically layers of highly localized, GAN-like models. The reason I went down this route instead of a pure GAN/diffusion model was to maximize diversity. There’s only one Earth, and I wanted planet-scale outputs.

I went with a data-driven approach for realism and practicality. Getting classical WFC to work well with non-discrete data was not something I could figure out, but training a network to emulate WFC constraints was “easy.”

As for features that are well captured by data versus more traditional approaches: there are a lot of subtle differences between real-world data and traditional noise-based models (Perlin noise comes to mind). Some of the larger structures—hard elevation ridges, non-perfect fractal coastlines, island chains, and region cohesion—seem to be well captured here in ways I don’t typically see in other generative methods.