xandergos/terrain-diffusion

Health v2 · maintenance only

Flags: prerelease_only

Adoption not part of the score

1365 stars · 85 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Terrain Diffusion is a Python framework that uses diffusion models as a learned, deterministic replacement for Perlin noise, generating infinite, randomly-accessible planetary terrain heightmaps in real time. Its core InfiniteDiffusion algorithm makes diffusion sampling lazy and unbounded with O(1) random access and seed-consistency, built on the infinite-tensor library.

Use cases

• generate infinite realistic terrain heightmaps for games
• replace Perlin noise with learned procedural terrain generation
• stream planetary-scale terrain and climate data in real time
• generate infinite panoramas with Stable Diffusion deterministically
• convert hand-drawn coarse maps into high-resolution heightmaps
• build Minecraft-style world generators with diffusion models

When to choose

• you need infinite, seed-consistent, randomly-accessible terrain generation with learned realism
• you want diffusion-model quality output at interactive rates on a consumer GPU
• you need a drop-in procedural-noise-like interface backed by neural models
• you want to study or reproduce the InfiniteDiffusion algorithm from SIGGRAPH '26

When to avoid

• you need a lightweight CPU-only solution with fast generation
• you only need simple classical noise like Perlin or simplex
• you need a stable production PyPI package (the published package is a 0.0.1 placeholder)
• you work on Mac and need GPU acceleration (Mac is CPU-only)

Facets

library · maturity active

machine-learning deep-learning llm-inference simulation graphics game-engine machine-learning deep-learning graphics simulation python windows cross-platform diffusion-models procedural-generation terrain-generation infinite-generation heightmaps pytorch research-paper siggraph game-development gpu linux macos

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