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README.md
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---
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license: apache-2.0
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tags:
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- world-model
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- minecraft
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- video-diffusion
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- action-conditioned
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pretty_name: ForgeWM Training Data
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size_categories:
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- 10K<n<100K
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---
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# ForgeWM Training Data
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Pre-encoded latents for training [ForgeWM](https://github.com/asdfo123/ForgeWM).
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This is a **re-packaging** of the [GameFactory](https://github.com/KlingAIResearch/GameFactory)
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GF-Minecraft dataset, encoded into Wan2.1 VAE latents and sharded as LMDB for
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direct use by ForgeWM training scripts. The underlying gameplay videos are
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from GameFactory — we just made them training-ready.
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## Quick Start
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```bash
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huggingface-cli download asdfo123/ForgeWM-data \
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--local-dir ./data/action_lmdb --repo-type dataset
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```
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Then point ForgeWM configs to `./data/action_lmdb`.
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## What's Inside
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| Field | Value |
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|---|---|
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| Clips | 4000 |
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| Shards | 10 LMDB files |
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| Latent shape | `(21, 16, 44, 80)` — Wan2.1 VAE, 84 frames @ 352×640 decoded |
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| Keyboard | one-hot W/S/A/D |
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| Mouse | `[yaw, pitch]`, normalized |
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| Total size | ~89 GB |
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**Processing note**: GameFactory's pitch convention (`+pitch = look-down`) is
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flipped to MG2's (`mouse[0] > 0 = look-up`) at encoding time.
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## Citation
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```bibtex
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@misc{forgewm2026,
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title={ForgeWM: A Reproducible Training Recipe for Action-Controllable World Models},
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author=ForgeWM Team,
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year={2026},
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url={https://github.com/asdfo123/ForgeWM}
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}
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```
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Please also cite **GameFactory** (the underlying data):
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```bibtex
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@misc{yu2024gamefactory,
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title={GameFactory: Creating New Games with Generative Interactive Videos},
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author={Yu, Jiwen and Qin, Yiran and Wang, Xintao and Wan, Pengfei and Zhang, Di and Liu, Xihui},
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year={2025},
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eprint={2501.08325},
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archivePrefix={arXiv},
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}
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```
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## License
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Apache 2.0 for this repackaging. The underlying GF-Minecraft data follows
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[GameFactory's license](https://github.com/KlingAIResearch/GameFactory) —
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please consult their terms before redistribution.
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```
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