Giga-World-Policy-0.5

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

• Project page: https://open-gigaai.github.io/giga-world-policy/

Model summary

Field	Value
Class	CasualWorldActionTransformer_MoT
Layers	30
Attention heads	24 × 128
Visual hidden dim	3072
Action expert dim	1024
Action FFN dim	4096
Latent channels	48 (in/out)
Action channels	16 (in/out)
Embodiments	2
Text dim (T5)	4096
Patch size	[1, 2, 2]

The MoT design keeps a visual expert stream (reference + future latents) and an action expert stream (state + action), with multi-modal self-attention across both.

Files

config.json
diffusion_pytorch_model.safetensors.index.json
diffusion_pytorch_model-00001-of-00003.safetensors
diffusion_pytorch_model-00002-of-00003.safetensors
diffusion_pytorch_model-00003-of-00003.safetensors

Weights are sharded at ~10GB per file. This repo contains the transformer only; runtime also needs the Wan2.2 VAE / scheduler from the base Diffusers checkpoint.

Download

# Hugging Face CLI
huggingface-cli download open-gigaai/Giga-World-Policy-0.5 --local-dir ./Giga-World-Policy-0.5

# or Git LFS
git lfs install
git clone https://huggingface.co/open-gigaai/Giga-World-Policy-0.5

Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="open-gigaai/Giga-World-Policy-0.5",
    local_dir="./Giga-World-Policy-0.5",
)

For usage, training, and inference details, see our open source code page.

Citation

@article{gigaworld-policy-0.5,
  title={GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch},
  author={Team, GigaWorld and Ye, Angen and Ma, Angyuan and Wang, Boyuan and Ni, Chaojun and Ye, Fangzheng and Huang, Guan and Li, Guo and Zhao, Guosheng and Yan, Haodong and others},
  journal={arXiv preprint arXiv:2607.13960},
  year={2026}
}