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Comment by derefr | Hacker News Reader

> something like the inner rules of that image and long range correlations

I assume that if you feed WFC a large input image, it just thinks of that as a very complex set of rules that are harder to satisfy than those of a small input?

Is there a way, then, to instead train the WFC algorithm on a large corpus of small, similar samples, such that it can try to derive the rules common to all the inputs in the corpus, and produce one image that "really" fits the rules, rather than just the ephemeral quirks from an individual sample?

Would there be, for example, a way to train WFC to produce outputs matching the level-design "aesthetic" of a given game, rather than just "continuing" a particular level?

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About harder and easier to satisfy, the question of how the rate at which the algorithm runs into contradictions depends on the input is not easy at all. There is no simple correlations between the contradiction rate and the size of the input.

But the first thing you'll notice if you feed it an image with a lot of patterns, is that it will work very slowly.

Yeah, the corpus thing can be done if we cut out rare patterns and leave only frequent ones. I haven't tried it though.

I wonder if it would be interesting to purposely search for tilesets that maximize contradiction rate. What would those things look like?

A very good question! The opposite of it is also important, can we follow some heuristics while creating tilesets to minimize contradiction rates, but not making tilesets easy? I don't know. If someone knows please tell me.

Hmm, this possibly relates to sheaf theory. Robert Ghrist has a good book about this stuff (applied topology) if you want to check it out.

>I assume that if you feed WFC a large input image, it just thinks of that as a very complex set of rules that are harder to satisfy than those of a small input?

Since the input is shredded into a multiset of N by M rectangles, it's the opposite: assuming the small input image is a portion of the large one, the large image model adds examples to the ones in the small image model, so the set of cases it can fit an example to is the same or larger.

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