Giga-World-Policy-0.5
GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
- Paper: GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
- Project page: https://open-gigaai.github.io/giga-world-policy/
- Repository: https://github.com/open-gigaai/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}
}