Talus: a 23M-parameter diffusion model for game terrain, evaluated against a real-vs-real noise floor, running in the browser on WebGPU [P]
Talus is a 23M-parameter diffusion model designed for generating game terrain, specifically 64x64 heightmaps (4 km, up to 1,200 m). It operates in the browser using WebGPU and was trained on an RTX 5060 (8 GB) for approximately 4.5 hours using 45,000 procedurally generated maps. Talus can be conditioned on terrain type and five properties: mean elevation, relief, mean slope, water fraction, and spectral slope. Current challenges include overly smooth mountains and grainy plains.
This model is notable for its small 23M parameter size and its evaluation against a real-vs-real noise floor, unlike many larger diffusion models.
Time & source
Times shown in UTC
Display time zone: UTC
Local time zone unavailable; showing UTC.
PublishedOffset at this time: UTC+0Oct 9, 2026, 19:52 UTC
IngestedOffset at this time: UTC+0Oct 9, 2026, 23:00 UTC
- Published
- Oct 9, 2026, 19:52
- Ingested
- Oct 9, 2026, 23:00
- Source type
- Dev community
- Tier
- Community
- Source status
- Sync delayed
Tier is a per-source editorial setting, not a per-item score.
Discussion trend
The percentage is based on collected discussion signal, not new comments or independent people. The curve only compares the same topic across time.
Talus generates 64x64 heightmaps (4 km, up to 1,200 m) conditioned on a terrain type and any subset of five measured properties: mean elevation, relief, mean slope, water fraction and spectral slope. I trained it from scratch on one RTX 5060 (8 GB), about 4.5 hours in total across versions. The data is 45,000 maps from my own procedural generator (fBm/ridged noise, stream-power erosion, hillslope diffusion, thermal erosion). Model
Pixel-space U-Net, v-prediction, cosine schedule, 50-step DDIM with quadratic spacing, classifier-free guidance 2.0
Each property has a learned "unknown" embedding and is dropped independently during training, so any subset works at inference
Relative heights: the model generates the map's shape normalized by its relief, and the sampler places it at the requested mean elevation and relief (borrowed from the 16 nearest training maps when unspecified). This fixed grainy plains: the plains distance ratio went from 3.98 to 1.23
Evaluation: Every distance (W1 over 25 per-map terrain metrics, radially averaged power spectrum, height and slope distributions) is divided by the same distance between two disjoint halves of real maps, so 1.0 means indistinguishable at that sample size. Checkpoints are chosen on VAL; TEST is scored once on the pick, and memorization is checked against the training set. Current model on TEST: metric W1 1.51x the floor, spectrum 9.1x, slopes 1.65x.
Browser: ONNX export with fp16-stored weights (cast to fp32 at load), ONNX Runtime Web on WebGPU, about 3 s per map on my RTX 5060, CPU fallback. A JavaScript reimplementation of the sampler matches PyTorch to within 0.6 m on reference samples. Demo: https://talus.tersa.tech Code, weights, scorecard (Apache-2.0): https://github.com/osfv/talus
Open problems: ridges and the finest spectral band, mountains too smooth and plains too grainy. If you've closed a spectrum gap like this in a small diffusion model, I'd like to hear what worked.