Instructions to use ShuyaoShang/PhiZero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use ShuyaoShang/PhiZero with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
PhiZero
Official checkpoints for PhiZero: A World Model Built Around Physical Language.
PhiZero reasons about future dynamics in a discrete Physical Language before rendering those dynamics as video.
Repository contents
.
βββ reasoner/
β βββ config.json
β βββ model-00001-of-00002.safetensors
β βββ model-00002-of-00002.safetensors
β βββ model.safetensors.index.json
β βββ tokenizer and processor files
βββ tokenizer/
βββ tokenizer.safetensors
βββ model_config.json
Physical Language Reasoner
reasoner/ contains the Qwen3-VL-based Physical Language Reasoner. Given a
first frame and an English motion caption, it predicts:
<bom> + 256 Physical Language symbols + <eom>
The Physical Language vocabulary contains 25,000 symbols, from <m_0> through
<m_24999>.
Physical Language Tokenizer and diffusion decoder
tokenizer/tokenizer.safetensors contains:
- the Physical Language Tokenizer;
- the inverse-FSQ and context-conditioning modules;
- the PhiZero DiT LoRA weights.
Keep tokenizer/model_config.json beside the checkpoint. The official
Wan2.2-TI2V-5B base DiT and VAE are not included here. They are resolved
separately by the PhiZero code or supplied through local Wan checkpoint paths.
Download
While this repository is private, authenticate with an account that has access:
hf auth login
Clone the PhiZero code and download all checkpoints into the layout expected by the release:
git clone https://github.com/yaoyao-jpg/PhiZero
cd PhiZero
hf download ShuyaoShang/PhiZero --local-dir ckpt
The resulting directory is:
PhiZero/
βββ ckpt/
βββ reasoner/
βββ tokenizer/
Download only the tokenizer/decoder checkpoint:
hf download ShuyaoShang/PhiZero \
tokenizer/tokenizer.safetensors \
tokenizer/model_config.json \
--local-dir ckpt
Download only the reasoner:
hf download ShuyaoShang/PhiZero \
--include "reasoner/*" \
--local-dir ckpt
Usage
Follow the installation, training, and inference instructions in the PhiZero code repository.
The default local checkpoint paths are:
export PHIZERO_REASONER_CHECKPOINT="${PWD}/ckpt/reasoner"
export PHIZERO_DECODER_CHECKPOINT="${PWD}/ckpt/tokenizer/tokenizer.safetensors"
Important note for motion transfer
The included tokenizer/tokenizer.safetensors is a general initialization
checkpoint and cannot be used directly for robot-domain or sim-to-real motion
transfer. First fine-tune it with videos from the same domain as the source
motion.
If no additional domain-matched data are available, the source videos can be used directly through reconstruction training. Target videos and paired source-target data are not required. See the source-domain adaptation section in the PhiZero code repository before running motion transfer.
Model settings
- Output resolution: 896 x 512
- Output length: 33 frames
- Frame rate: 8 FPS
- Physical Language length: 256 symbols
- Physical Language codebook size: 25,000
- Default diffusion steps: 50
Intended use and limitations
PhiZero is intended for research on physical-dynamics reasoning, video world models, reconstruction, and motion transfer.
It is not a guaranteed physical simulator. Generated dynamics can be inaccurate, unstable, or inconsistent with real-world constraints. Results should not be used directly for safety-critical control or decision-making.
Inference requires the custom PhiZero code. These checkpoints are not intended to run through the standard hosted Hugging Face inference widget. Users must also comply with the licenses and terms of the upstream base models.
Citation
@article{shang2026phizero,
title = {PhiZero: A World Model Built Around Physical Language},
author = {Shang, Shuyao and Wang, Yuqi and Gao, Ruopeng and Chen, Xu and
Tan, Tieniu and Fan, Lue and Zhang, Zhaoxiang},
journal = {arXiv preprint arXiv:2607.28624},
year = {2026}
}
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Qwen/Qwen3-VL-4B-Instruct