Parallel Jacobi Decoding for Fast Autoregressive Image Generation

Boya Liao, Ying Li, Siyong Jian, Huan Wang^*

📖Paper Teaser

Images generated by Lumina-mGPT using vanilla autoregressive decoding (left) and our Parallel Jacobi Decoding (right). Our method reduces the required autoregressive steps by up to 6.8× while maintaining visual fidelity..

Installation

Clone the repository and create a Conda environment:

git clone git@github.com:boyaliao/PJD.git
cd PJD

conda create -n pjd python=3.10 -y
conda activate pjd

pip install -r requirements.txt
pip install -e .

Usage

1. Download the Image Tokenizer

This project uses the image tokenizer from Meta's Chameleon model. Download the required tokenizer files from the Meta Chameleon repository.

Place the downloaded files in the following directory:

ckpts/chameleon/tokenizer/

The final directory structure should be:

ckpts/
└── chameleon/
    └── tokenizer/
        ├── checklist.chk
        ├── text_tokenizer.json
        ├── vqgan.ckpt
        └── vqgan.yaml

Note: Make sure all four files are present before running inference.

Acknowledgments

This implementation is built upon the official repository for SJD.

Citation

@inproceedings{liao2026parallel,
  title={Parallel Jacobi Decoding for Fast Autoregressive Image Generation},
  author={Liao, Boya and Li, Ying and Jian, Siyong and Wang, Huan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}