Official PyTorch implementation for the ICCV 2025 paper "Grouped Speculative Decoding for Autoregressive Image Generation (GSD)"

• Release Arxiv Paper
• Release Initial Codebase
• Release Colab tutorial
• Apply to more AR models (e.g., Lumina mGPT 2)

Our model uses the image tokenizer from Meta's Chameleon. Please download the necessary files from Meta's Chameleon homepage.

Place the downloaded files into the ckpts/chameleon/tokenizer/ directory. The final folder structure should look like this:

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

We recommend using PyTorch >= 2.3.0. Install the required packages with pip:

pip install transformers==4.48.1 sentencepiece accelerate>=0.26.0 absl-py

To generate images with our GSD model, run the following command. You can change the prompt directly within the test_GSD.py file.

python test_GSD.py

To compare the speed with the SJD (ICLR 2025) baseline, run this command:

python test_SJD.py

This implementation is heavily based on the official repository for SJD by tyshiwo1: tyshiwo1/Accelerating-T2I-AR-with-SJD.