Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
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Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#2This is really awesome, actually! It looks great, very diverse, and clearly is scalable to extremely large maps. Props for testing the generation out on Minecraft - where terrain generation really matters.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#3I wonder what it would take to adapt a model like this to generate non-Earthlike terrain. For example, if you were using it to make planets without atmospheres and without water cycles, or planets like Io with rampant volcanism.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#4This is really awesome, actually! It looks great, very diverse, and clearly is scalable to extremely large maps. Props for testing the generation out on Minecraft - where terrain generation really matters.
No it's not. The whole presentation of it is confusing. Sorry.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#5It's interesting but i can't see it replacing Perlin or Simplex noise which are used for more than just terrain generation.
edit: I don't think i have the vocabulary to describe other issues i have other than it doesn't feel like the right way to "solve" this problem.
I'd prefer something that was entirely code rather than requiring training, and possibly retraining to get what i want.
edit2: Also is this entirely flat ? or can it be applied to a sphere (planet) , or terrain inside a cylinder (rotating space habitat) ?
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#6I worked on something very similar for my master's degree.
The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either.
In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation.
It'll be interesting to see of the authors follow up this paper with research into even stronger ability to condition and control terrain outputs.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#7Convincing AND useful procedural terrain is usually hard-simulated along some manually placed guides, which is typically faster and more versatile than a diffusion model. I don't see any model being used in practice for this, at least not until it has good controlnets trained specifically for this task. However something like this can be useful for texture generation, especially with geometry/camera position/lighting as additional inputs.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#8I worked on something very similar for my master's degree. The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either. In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation. It'll…
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Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#9Mm. This paper makes it hard to understand what they've done.
For example:
> MultiDiffusion remains confined to bounded domains: all windows must lie within a fixed finite canvas, limiting its applicability to unbounded worlds or continuously streamed environments.
> We introduce InfiniteDiffusion, an extension of MultiDiffusion that lifts this constraint. By reformulating the sampling process to operate over an effectively infinite domain, InfiniteDiffusion supports seamless, consistent generation at scale.
…but:
> The hierarchy begins with a coarse planetary model, which generates the basic structure of the world from a rough, procedural or user-provided layout. The next stage is the core latent diffusion model, which transforms that structure into realistic 46km tiles in latent space. Finally, a consistency decoder expands these latents into a high-fidelity elevation map.
So, the novel thing here is slightly better seemless diffusion image gen.
…but, we generate using a heirsrchy based on a procedural layout.
So basocally, tldr: take perlin noise, resize it, and then image-2-image use it as a seed to generate detailed tiles?
People have already been doing this.
Its not novel.
The novel part here is making the detailed tiles slightly nicer.
Eh. :shrug:
The paper obfuscates this, quite annoyingly.
Its unclear to me why you cant just use multi diffusion for this, given your top level input is already bounded (eg. User input) and not infinite.
Re: Terrain Diffusion: A Diffusion-Based Successor to Perlin Noise
#10I worked on something very similar for my master's degree. The problem I could never solve was the speed, and from reading the paper it doesn't seem like they managed to solve that either. In the end, for my work, and I expect for this work, it is only usable for pre generated terrains and in that case you are up against very mature ecosystems with a lot of tooling to manipulate and control terrain generation. It'll…
I came here to say this. My masters was on procedural generation. Perlin, fBm, etc. The things these noise functions have that an LLM doesn’t is speed. 1-D perlin is just a dozen or so multiplications with a couple random coefficients. The GPU can do 4-D Perlin all day long every frame taking up a 4096x4096x32 texture volume.
While I do like the erosion effects and all, having a few height texture brushes that have those features that you can multiply on the GPU is trivial. I still welcome these new approaches but like you said, it’s best for pre generation.