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ForgeWM Training Data
Pre-encoded latents for training ForgeWM.
This is a re-packaging of the 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
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
@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):
@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 — please consult their terms before redistribution. ```
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