| --- |
| library_name: pytorch |
| tags: |
| - world-model |
| - diffusion |
| - dit |
| - meanflow |
| - counter-strike |
| - opencs2 |
| --- |
| |
| # CS2 World Model — action-conditioned latent DiT (OpenCS2) |
|
|
| Action-conditioned latent world model for OpenCS2: given a few recent frames and the player's |
| actions, it predicts the next frame. It never touches pixels — history, target and prediction are |
| all `32×8×8` latents produced by the frozen [CS2 RAE tokenizer](https://huggingface.co/blanchon/cs2-rae). |
| Code: [github.com/julien-blanchon/world-model](https://github.com/julien-blanchon/world-model). |
|
|
| - **Dynamics core:** ~205M-param in-context DiT (`hidden=1024`, `depth=16`, `heads=16`, |
| `register_tokens=4`, always-on within-frame conv-stem). |
| - **Transport:** iMeanFlow with plain-Gaussian noise (no OT coupling), schedule-shift `α=2`, |
| plain MSE. A frame samples in **one network evaluation (NFE=1)**. |
| - **Conditioning:** `history_frames=4`, `sequence_frame_gap=1`, action-conditioned. |
| - **Training:** AdamW lr `2e-4`, warmup 2000, EMA `0.9995`, DIAMOND scheduled sampling with an |
| `nas` depth curriculum (1 → 2 → 4) and a context-noise ramp (0.05 → 0.2). |
|
|
| ## Files |
|
|
| | File | Description | |
| |---|---| |
| | `world-model.pt` | Final world-model checkpoint (`runs/world_model/world-model.pt`). | |
|
|
| ## Usage |
|
|
| Requires the matching [CS2 RAE tokenizer](https://huggingface.co/blanchon/cs2-rae) (`d32`) and the |
| [`world-model`](https://github.com/julien-blanchon/world-model) codebase. |
|
|
| > Internal research checkpoint. Provided as-is, no warranty. |
|
|
