README.md

---
license: apache-2.0
tags:
  - world-model
  - minecraft
  - video-diffusion
  - action-conditioned
pretty_name: ForgeWM Training Data
size_categories:
  - 10K<n<100K
---

# ForgeWM Training Data

Pre-encoded latents for training [ForgeWM](https://github.com/asdfo123/ForgeWM).

This is a **re-packaging** of the [GameFactory](https://github.com/KlingAIResearch/GameFactory) 
GF-Minecraft dataset, encoded into Wan2.1 VAE latents and sharded as LMDB for 
direct use by ForgeWM training scripts. The underlying gameplay videos are 
from GameFactory — we just made them training-ready.

## Quick Start

```bash
huggingface-cli download asdfo123/ForgeWM-data \
  --local-dir ./data/action_lmdb --repo-type dataset
```

Then point ForgeWM configs to `./data/action_lmdb`.

## What's Inside

| Field | Value |
|---|---|
| Clips | 4000 |
| Shards | 10 LMDB files |
| Latent shape | `(21, 16, 44, 80)` — Wan2.1 VAE, 84 frames @ 352×640 decoded |
| Keyboard | one-hot W/S/A/D |
| Mouse | `[yaw, pitch]`, normalized |
| Total size | ~89 GB |

**Processing note**: GameFactory's pitch convention (`+pitch = look-down`) is 
flipped to MG2's (`mouse[0] > 0 = look-up`) at encoding time.

## Citation

```bibtex
@misc{forgewm2026,
  title={ForgeWM: A Reproducible Training Recipe for Action-Controllable World Models},
  author=ForgeWM Team,
  year={2026},
  url={https://github.com/asdfo123/ForgeWM}
}
```

Please also cite **GameFactory** (the underlying data):
```bibtex
@misc{yu2024gamefactory,
      title={GameFactory: Creating New Games with Generative Interactive Videos}, 
      author={Yu, Jiwen and Qin, Yiran and Wang, Xintao and Wan, Pengfei and Zhang, Di and Liu, Xihui},
      year={2025},
      eprint={2501.08325},
      archivePrefix={arXiv},
}
```

## License

Apache 2.0 for this repackaging. The underlying GF-Minecraft data follows 
[GameFactory's license](https://github.com/KlingAIResearch/GameFactory) — 
please consult their terms before redistribution.
```