README.md

---
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](https://huggingface.co/papers/2505.10496).

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](https://huggingface.co/papers/2505.10496)
- **Project Page:** [https://raman1121.github.io/CheXGenBench/](https://raman1121.github.io/CheXGenBench/)
- **Repository:** [https://github.com/Raman1121/CheXGenBench](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](https://github.com/Raman1121/CheXGenBench).

## Citation
If you find this model or the benchmark useful in your research, please cite:

```bibtex
@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}
}
```