Add model card for Sana 0.6B (CheXGenBench)

README.md

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+---
+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}
+}
+```