license: apache-2.0
pipeline_tag: image-to-image
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
This repository contains FlowMo, a transformer-based diffusion autoencoder that achieves state-of-the-art performance for image tokenization at multiple compression rates. It is introduced in the paper Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization.
FlowMo operates without using convolutions, adversarial losses, spatially-aligned two-dimensional latent codes, or distilling from other tokenizers. Its key insight is that training should be broken into a mode-matching pre-training stage and a mode-seeking post-training stage.
Links
- Project Page: https://kylesargent.github.io/flowmo
- Code Repository: https://github.com/kylesargent/FlowMo
Usage
The official GitHub repository provides comprehensive instructions for installation, data preparation, training, and evaluation. A Jupyter notebook, example.ipynb, is available to demonstrate how to use the FlowMo tokenizer for image reconstruction.
Citation
If you find FlowMo useful, please cite our paper:
@misc{sargent2025flowmodemodeseekingdiffusion,
title={Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization},
author={Kyle Sargent and Kyle Hsu and Justin Johnson and Li Fei-Fei and Jiajun Wu},
year={2025},
eprint={2503.11056},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.11056},
}