Observed since 20 Oct, 19:08 UTC
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Fast difficulty-conditioned terrain generation with ONNX Runtime
Fast, difficulty-conditioned terrain generation with an endpoint-distilled one-step model and ONNX Runtime.
The teaser above shows all 100 difficulties across six simulator renderers.
pip install adaptive-terrain-diffusion
pip install "adaptive-terrain-diffusion[render,images]" # optional rendering and TIFF support
from terrain_diffusion import TerrainGenerator, render_terrains, save_terrains
generator = TerrainGenerator() # automatically selects CUDA when available
terrains, difficulties = generator.generate(
4,
shape=(256, 384),
difficulty="random", # or any integer from 1 to 100
seed=7,
return_difficulties=True,
)
variations = generator.generate_from(
terrains[0], difficulty=75, strength=0.5, variants=4
)
save_terrains("terrains", terrains, suffix=".npy")
render_terrains(terrains, save_path="terrains.png")
generate_from also accepts batches and .txt, .csv, .npy, .npz, or TIFF files. Larger-than-native maps use overlapping, blended generation automatically.
terrain-diffusion generate --output terrains --num 4 --difficulty 60 --shape 256 384 --render
terrain-diffusion vary terrain.npy --output variants --difficulty 75 --variants 4
terrain-diffusion simulate mujoco --difficulty 100 --shape 1024 1024
SimulatorTerrain.apply() supports IsaacLab, MuJoCo, MuJoCo Playground, MuJoCo Warp, PyBullet, and Gazebo without adding simulator packages as dependencies.
from terrain_diffusion import SimulatorTerrain
asset = SimulatorTerrain(terrains[0], horizontal_scale=0.1)
asset.apply("pybullet", client_id)
See the simulator tutorial for complete integrations.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.4.2 | 12 Aug 2026 | 7 |
This describes our collected data; it is not a security assessment.
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Observed since 20 Oct, 19:08 UTC
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package:ea25e9ffe12ad1a1b0a376790709063138016c2b0483fb55145944246249c21f