wfc

Quantum WaveFunctionCollapse

| Boris

One of my biggest gripes with the WaveFunctionCollapse procedural generation algorithm is that, despite the name, it doesn’t really have anything to do with quantum mechanics. I usually prefer the term Constraint Based Procedural Generation instead.

The name WaveFunctionCollapse is meant more as an analogy. As the algorithm progresses, it resolves a fuzzy, uncertain picture of the output into sharper detail, much as in quantum mechanics, the state of a system is also a range of possibilities, which resolves to something specific when “observed”.

But could we adapt WFC to the Quantum way of thinking, and ran it on actual Quantum Hardware? Well, that’s exactly what is discussed in this new paper Quantum WaveFunctionCollapse by Raoul Heese1 (Youtube summary). Does it work? Is it fast? Let’s find out.

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Editable WFC

| Boris

When I spoke about autotiling, I briefly touched on how it’s possible to use Wave Function Collapse (or other constraint based generators) as a form of autotiling, i.e. user-directed editing of tilemaps.

I’ve usually referred to this technique as “editable WFC“. It’s a combination of autotiling and WFC, and contains the best of both:

  • Being an autotiler, it allows users to easily and interactively make changes to an existing level.
  • Being constraint based, it automatically ensures that those changes are consistent with the predefined rules of the constraints, potentially making further changes to the level to make it fit

This is different from most other autotilers, which either require manual configuration of patterns used to enforce good behaviour, hidden layers, or come with more stringent requirements on what tiles are available.

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Infinite Modifying in Blocks

| Boris

I’m going to share with you a technique I’ve found for doing lazy, reliable, deterministic, constant-time infinite generation of tile based levels using Wave Function Collapse (WFC). But first, let’s cover some background, Modifying in Blocks, and lazy chunk based infinite generation.

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Constraint-Based Tile Generators

| Boris

Last article, we were comparing WaveFunctionCollapse (WFC), and Model Synthesis (MS). These are both similar procedural generation techniques that work along similar lines. Specifically, they generate a grid of tiles (or pixels), using a set of hard constraints, and some generalized solver technique to find a solution, a set of tiles that satisfies all the constraints provided. Let’s call this overall class of thing constraint-based tile generators.

The techniques have been pretty popular in recent years, and part of that is because it’s actually a very easy concept to experiment with and extend. Many of the more practical examples in the wild include their own extensions, as basic WFC tends to produce levels that look a bit repetitive and structureless.

Today I thought I’d do a review of all the different ways that you can customize this generation technique, showing how WFC and MS are subsets of a greater class. The whole family of such algorithms is much larger and I think there’s a lot of potential still to explore.

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Model Synthesis and Modifying in Blocks

| Boris

I’ve written a lot about Wave Function Collapse. Developed in 2016 by Maxim Gumin, it’s an algorithm for generating tilemaps and pixel textures based on the constraint solving with extra randomization . But did you know most of the key ideas come from a paper written a full decade earlier? Today, we’ll be looking into Model Synthesis, the 2007 PhD dissertation of Paul Merrell, and some of the elaborations he’s designed, particularly Modifying in Blocks.

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Driven WaveFunctionCollapse

| Boris

WaveFunctionCollapse (WFC) is a procedural generation technique for creating images and tile-based levels. I’ve discussed it many times before.

As a technique, it has some pros and cons. Pro: it’s almost uncannilly good at stitching together tilesets into interesting arrangements, and is pretty good at copying the style in a supplied sample image. Cons: it becomes bland and repetitive at large scales.

In my software Tessera, I’ve been working on various ways of customizating the generation to work around that con. But I’ve seen another way that turns WFC on its head. Instead of using WFC as a full level generator, we want to decide the overall structure of a level some other way, and then use WFC just for the details.

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Wave Function Collapse Explained

| Boris

A simple guide to constraint solving

Since developing DeBroglie and Tessera, I’ve had a lot of requests to explain what it is, how it works. The generation can often seem quite magical, but actually the rules underlying it are quite simple.

So, what is the Wave Function Collapse algorithm (WFC)? Well, it’s an algorithm developed by Maxim Gumin based on work by Paul Merrell for generating tile based images based off simple configuration or sample images. If you’ve come here hoping to learn about quantum physics, you are going to be disappointed.

WFC is capable of a lot of stuff – just browse Maxim’s examples, or check out #wavefunctioncollapse on twitter, or see my youtube video.

WFC is explained briefly in Maxim’s README, but I felt it needed a fuller explanation from first principals. It is a slight twist on a much more broad concept – constraint programming. So much of this article is going to explain constraint programming, and we’ll get back to WFC at the end.

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Wave Function Collapse tips and tricks

Dungeon generation with pathing
| Boris

I’ve been experimenting a lot with constraint-based procedural generation these days. Specifically the Wave Function Collapse algorithm (WFC). I’ve even made my own open source library, and unity asset.

WFC is a very flexible algorithm, particularly with the enhancements I’ve designed, but at the same time, I’ve found it’s quite hard to actually get it to produce practical levels useful for computer games. The key difficulty is WFC doesn’t have any global structure to it, all it does it make the output generation look like the input locally, i.e. when viewing small rectangles of output at a time.

In this article, I share what I’ve learned to take your constraint based generators to the next level.

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