WorldCast: Distributed Multiplayer World Models
Ziyang Ye1, Junchao Huang1,2, Evelyn Zhang2, Zhihao Xie1, Ruicheng Zhang3, Boyao Han1, Litao Ban4, Ziye Wang4, Xinting Hu5, Shaoshuai Shi4, Zhuotao Tian2, Li Jiang1,2†
1CUHK-Shenzhen 2SLAI 3Tsinghua SIGS 4Voyager Research, Didi Chuxing 5USTC †Corresponding author
Project page | Code | Paper
WorldCast is a distributed multiplayer world model. Each player runs a local client, a video generator fine-tuned from Wan2.2-TI2V-5B, on its own GPU. Clients exchange only player states, which each client projects into a camera-aligned player state field, and a shared scene state of generated blocks. This repository holds the weights of the release: the final 4-step model first, with everything the inference code loads beside it.
Files
| file | what | dtype | size | sha256 |
|---|---|---|---|---|
worldcast_4step_bf16.safetensors | the final model: the 4-step student (stage 4, distribution matching distillation, 600 steps), EMA weights; the generator the inference code runs (paper Table 3) | bf16 | 10.2 GB | 7cfd85b59de04968814a5d560cf6258c91ede0d3a850bd759fe5cc91c5c35b31 |
state_model.safetensors | the state model: encoder, trunk, action encoder, motion head and place head (paper Sec. 3.4); a client's estimate of its own position from the latents it generates (player_state.source: predicted) | fp32 | 1.4 GB | 86fbf7e11418c568c2a33c8163774b52d1392dde9619e0dc5d9b277251547b23 |
depth_head.safetensors | picture depth head of the scene state (depth of each generated block) | fp32 | 176.8 MB | ab599fcd68115142cf3b946e147f3cb89465d0b389371cc3501e1eb3101f6e76 |
depth_readout.safetensors | read-out of the depth head | fp32 | 833.4 kB | 6994480d726b7e958f519e835fa306b7045cfb19171ade4aebcb594a67ccd873 |
fixed_prompt_umt5xxl_bf16.safetensors | umT5-XXL embedding of the fixed prompt (the client then skips the 11 GB text encoder) | bf16 | 4.2 MB | a4157803a2c381835b219c079b7d811b7950c3fb2c47741d4552b7bba37cbd0a |
config.json | the release's description: the weight files by the paths key of the inference code, and the base model with its pinned revision | - | 0.5 kB | |
examples/ | inputs and expected videos of six recorded rounds (examples/run.sh of the code); listed in examples/manifest.json of the code | - | 201.6 MB |
The generator file holds EMA weights under the parameter names of the release model (WorldCastGenerator), so a
strict load_state_dict works. The inference code needs the five weight files above and nothing else of this
repository.
Quick start
git clone https://github.com/Ziyang-Ye/WorldCast && cd WorldCast
pip install -e .
python tools/download_weights.py --out-dir weights
This fetches the inference files above, plus the Wan2.2 VAE, tokenizer and config.json from
Wan-AI/Wan2.2-TI2V-5B, and writes weights/paths.yaml. Running a session, the examples, and the
options of the download: see the code repository.
License and attribution
- The WorldCast weights are released under the Apache License 2.0.
- They are fine-tuned from Wan2.2-TI2V-5B (Apache License 2.0).
- Training data: the OpenCS2 dataset
(CC BY 4.0). The files under
examples/are derived from it. - The model renders Counter-Strike 2 game content and is intended for research. Counter-Strike 2 is a trademark of Valve Corporation; this work is not affiliated with or endorsed by Valve.
Citation
@article{ye2026worldcast,
title = {WorldCast: Distributed Multiplayer World Models},
author = {Ye, Ziyang and Huang, Junchao and Zhang, Evelyn and Xie, Zhihao and Zhang, Ruicheng and Han, Boyao and Ban, Litao and Wang, Ziye and Hu, Xinting and Shi, Shaoshuai and Tian, Zhuotao and Jiang, Li},
journal = {arXiv preprint arXiv:2610.12412},
year = {2026}
}