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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Minimalist, Batteries-included Repository for Advancing World Model Science.
| Date | Stars |
|---|---|
| 2026-07-24 | 690 |
| 2026-07-25 | 692 |
| 2026-07-28 | 694 |
| 2026-07-30 | 694 |
| 2026-08-06 | 694 |
Today
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Momentum
0.0
growth rate 0.00%/day
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<h1>🌍 Nano World Model</h1>
</div>
<div align="center">
<a href='https://arxiv.org/abs/2605.23993'><img src='https://img.shields.io/badge/arXiv-2605.23993-b31b1b.svg'></a>
<a href='https://huggingface.co/collections/knightnemo/nano-world-model'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Page-blue'></a>
<a href='https://simchowitzlabpublic.github.io/nano-world-model/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"/></a>
</div>
A minimalist repository for training video world models based on diffusion-forcing.
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</div>
## News and Updates
- 🚩 **2026-05:** We release [📕 Technical Report](https://arxiv.org/abs/2605.23993) on arxiv, check it out!
- 🚩 **2026-05:** We release all model checkpoints on [🤗 huggingface](https://huggingface.co/collections/knightnemo/nano-world-model).
- 🚩 **2026-05:** We release code for training, inference and evaluation.
## Key Features
- 🚀 **Instant Start** — Minimal dependencies, easy data loading. From clone to first rollout in minutes.
- 🛠️ **Unified Pipeline** — Training, Validation, Evaluation; All managed with clean hydra-based configuration systems.
- 🔬 **Scientific Transparency** — Clean codebase with head-to-head ablations across prediction target, action injection, and model scale; Fully open-source, including model checkpoints.
- 🤖 **Diverse Applications** — Long-horizon rollouts, rollout to 3d point clouds, planning (MPC) out of the box.
## 🚀 Quick Start
```bash
git clone https://github.com/simchowitzlabpublic/nano-world-model.git
cd nano-world-model
conda env create -f environment.yml && conda activate nanowm
```
LeRobot dataset loading is provided by `lerobot==0.3.3` in `environment.yml`.
Do not install `lerobot-datasets`; it is a dataset format version, not a PyPI package.
Set data + results paths (or use the gitignored `src/configs/local/paths.yaml` template — see [docs/config_system.md](docs/config_system.md#path-configuration)):
```bash
export DATASET_DIR=/path/to/dino_wm_data # DINO-WM envs (point_maze, pusht, ...)
export CSGO_DATA_DIR=/path/to/csgo # CSGO HDF5 files
export RT1_DATA_ROOT=/path/to/rt1_fractal # RT-1 LeRobot mirror (optional)
export RESULTS_DIR=/path/to/results # checkpoints + logs land here
```
Download the i3d torchscript used by FID/FVD evaluation:
```bash
mkdir -p pretrained_models/i3d && curl -L \
"https://www.dropbox.com/scl/fi/c5nfs6c422nlpj880jbmh/i3d_torchscript.pt?rlkey=x5xcjsrz0818i4qxyoglp5bb8&dl=1" \
-o pretrained_models/i3d/i3d_torchscript.pt
```
For dataset downloads (DINO-WM, RT-1, CSGO), see [docs/datasets/README.md](docs/datasets/README.md).
## 🥷 Train your first model
DINO-WM PushT, NanoWM-B/2, default settings (pred-v · additive injection · cosine + ZTSNR):
```bash
python src/main.py experiment=dino_wm_pusht dataset=dino_wm/pusht model=nanowm_b2
```
CSGO with the L/2 model:
```bash
python src/main.py experiment=csgo dataset=game/csgo model=nanowm_l2_csgo
```
RT-1 (fractal) main run:
```bash
python src/main.py experiment=rt1 dataset=rt1/rt1 model=nanowm_b2
```
For reproducibility, we provide example scripts in `src/scripts/`. See [docs/training.md](docs/training.md) for the full training guide, design choices, and ablation tables.
## 📦 Pretrained Checkpoints
Best-config runs (pred-v · additive · cosine + ZTSNR · NanoWM-B/2 unless noted):
<div align="center">
| Domain | Checkpoint | Steps |
|:-------|:-----------|:------|
| DINO-WM Point Maze | 🤗 [nanowm-b2-dino-wm-point-maze-30k](https://huggingface.co/knightnemo/nanowm-b2-dino-wm-point-maze-30k) | 30k |
| DINO-WM Wall | 🤗 [nanowm-b2-dino-wm-wall-15k](https://huggingface.co/knightnemo/nanowm-b2-dino-wm-wall-15k) | 15k |
| DINO-WM Rope | 🤗 [nanowm-b2-dino-wm-rope-15k](https://huggiExcerpt of 8,107 characters
Read on GitHub
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George Pickett · United States
1
Ruixing Zhang
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0afbe7233f7f6b27, topic:diffusion-models
matched fp:0afbe7233f7f6b27, topic:video-generation