nanoOasis

nanoOasis — weights

Weights for nanoOasis, a from-scratch reference implementation of a diffusion world model — the "nanoGPT" of the Oasis / GameNGen / DIAMOND paradigm. The model generates the game Snake one frame at a time in response to your arrow keys: there is no game engine.

▶ Play the live demo · Code + docs

Files

How it works

A ViT-VAE compresses each 256×192 frame to 48 latent tokens (the game is an 8×6 grid, so one cell = one DiT token). A 13.5M-parameter spatiotemporal DiT predicts the next latent from the past 8 latents + your action, trained with EDM preconditioning + Diffusion Forcing + context-noise augmentation. Four Euler sampling steps make it real-time, so there's no distillation. At play time the model is the only thing generating frames; a ~30-line deterministic "referee" adjudicates wall/self collisions (the discrete events diffusion models handle unreliably).

Trained end-to-end for under $50 on ~500k frames of bot-played Snake.

Usage

git clone https://github.com/MaruthiV/nanoOasis && cd nanoOasis
pip install -e .

# play it locally (pygame window, arrow keys)
python infer.py --ckpt dit.pt --vae vae.pt --config small

The ONNX files are what the in-browser demo runs; see export.py and demo/ in the repo.

Intended use & limitations

A reference implementation for learning and forking the diffusion-world-model recipe — not a product. It's a 13.5M-parameter model trained for ~$50, and it plays like one: crisp Snake for the first several apples, then long-body coherence frays (diffusion models fumble long thin structures, and error accumulates over a rollout). The demo re-seeds a clean context on death so each life starts fresh.

License

MIT. Built in the lineage of DIAMOND, GameNGen, and Oasis, in the spirit of nanoGPT.