texture-synthesis VS fast-wfc

I found the (except when trained exclusively on work you've created during the jam) remark pretty funny, but I suppose someone could train a smaller generative model in the allotted time time (or perhaps use something like texture-synthesis on their own artwork; not sure if that counts as AI).

The submitted article goes above and beyond to guarantee that the code always output the same answer even though it has race conditions. But that's sometimes not possible or, if it's possible, it's too much of a hassle so it's rarely done.

For example, this project https://github.com/EmbarkStudios/texture-synthesis generates textures and if you run the same code with the same input various times, the results will be slightly different. Here https://github.com/EmbarkStudios/texture-synthesis#notes it says: "When using multiple threads for generation, the output image is not guaranteed to be deterministic with the same inputs. To have 100% determinism, you must use a thread count of one"

We are at Embark! https://embark.dev/, https://embark.rs, https://embark.games.

I tried one time to make my own WFC implementation in C++, and it didn't work and I ended up using this, what references you used?, btw great job

I used this algorithm to generate minigolf courses [1].

I used this repo [2].

The algorithm is literally just a constraint solver. I'm pretty sure it's pretty similar to the sudoku solver I wrote in prolog for a university course.

It's kinda pretentiously named and described, probably because its more academic to do that. It's just a constraint solver.

1: https://twitter.com/00jknight/status/1249091532071645184

2: https://github.com/math-fehr/fast-wfc