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---
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.
``` |