library_name: diffusers
pipeline_tag: text-to-image
Sana 0.6B (CheXGenBench)
This repository contains the Sana 0.6B model checkpoint, which was identified as a top-performing architecture for synthetic chest radiograph generation in the paper CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs.
CheXGenBench is a rigorous and multifaceted evaluation framework that assesses synthetic chest radiograph generation across fidelity, privacy risks, and clinical utility. Sana 0.6B achieved state-of-the-art results on this benchmark and was used to generate the SynthCheX-75K dataset.
- Paper: CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs
- Project Page: https://raman1121.github.io/CheXGenBench/
- Repository: https://github.com/Raman1121/CheXGenBench
Model Description
Sana 0.6B is a text-to-image generative model capable of producing high-fidelity medical imagery. In the context of the CheXGenBench benchmark, it demonstrated superior performance in capturing clinical details while maintaining a balance between generation quality and privacy.
For detailed instructions on environment setup, generating synthetic data, and running evaluation metrics (FID, privacy, and clinical utility), please refer to the official GitHub repository.
Citation
If you find this model or the benchmark useful in your research, please cite:
@article{dutt2025chexgenbench,
title={CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs},
author={Dutt, Raman and Sanchez, Pedro and Yao, Yongchen and McDonagh, Steven and Tsaftaris, Sotirios A and Hospedales, Timothy},
journal={arXiv preprint arXiv:2505.10496},
year={2025}
}