FastNoise VS PyFastNoiseLite

If you go to the FastNoiseLite github you'll see there's a FastNoiseLite.glsl file, this file contains the entire library. So to use this in a shader we need to use the new shader #inlude preprocessor. In short, create a new ShaderInclude resource, paste the contents of the FastNoiseLite.glsl file into it and then simply #include it in your shader.

FastNoiseLite: ValueNoise, OpenSimplex2, OpenSimplex2S, Perlin, ValueCubic (from https://github.com/Auburn/FastNoiseLite )

Fair enough. If it helps, you can drop this into a P5.js sketch without too much friction: https://github.com/Auburn/FastNoiseLite/tree/master/JavaScript

I suppose I'll compile what I can find here for future searchers: Here's the original github for FNL , it has some images to use as examples but few explanations unless you want to download and run the preview program yourself.

Also take a look at FastNoiseLite if you want better control over noise generation, and for cool examples.

I used this repo: https://github.com/Auburn/FastNoiseLite

Yeah, that's literally what a seed does. It's really odd to me that the unity mathematics one doesn't have a seed. I use this one: FastNoiseLite

I really like this library which appears to have gals implementation. https://github.com/Auburn/FastNoiseLite

For the actual dunes I used fast noise lite ( https://github.com/Auburn/FastNoiseLite ). They have a nice gui app, where you can create noise textures (in my case voronoi noise). Then I apply the height map to the individual terrain tiles. So no fancy procedural Generation is going on here.

/u/FMWizard I would try this https://github.com/tizilogic/PyFastNoiseLite/ with these configs. It's a C wrapper, so it should be faster than the python libraries.

Another piece of info you might find interesting is that the traditional approach of warping, where you distort each axis individually, isn't the only way to do that. The choice of noise function certainly makes more visual impact, but there is more directionally-uniform way to domain-warp too: explicitly-designed vector-output noise. Such noise chooses a random output-space direction to apply to each internal gradient ramp contribution, which results in a warp distribution that's effectively radially-symmetric. It also only takes one noise evaluation instead of two or three. I wrote this as part of my contributions to FastNoiseLite. There does exist PyFastNoiseLite as a wrapper for that, though it unfortunately doesn't currently have wrappers for the domain warping functions.