Update README.md

README.md

@@ -14,6 +14,26 @@ license: openrail++
 
 # OBS-Diff Structured Pruning for Stable Diffusion-xl-base-1.0 
 
+<table>
+  <tr>
+    <td width="30%">
+      <img src="teaser.jpg" alt="OBS-Diff" width="100%" />
+    </td>
+    <td width="70%">
+      <h4>✂️ <a href="https://alrightlone.github.io/OBS-Diff-Webpage/">OBS-Diff: Accurate Pruning for Diffusion Models in One-Shot</a></h4>
+      <p>
+        <em><b>Junhan Zhu</b>, Hesong Wang, Mingluo Su, Zefang Wang, Huan Wang*</em>
+        <br>
+        <a href="https://arxiv.org/abs/2510.06751"><img src="https://img.shields.io/badge/Preprint-arXiv-b31b1b.svg?style=flat-square"></a> 
+        <a href="https://github.com/Alrightlone/OBS-Diff"><img src="https://img.shields.io/github/stars/Alrightlone/OBS-Diff?style=flat-square&logo=github"></a>
+      </p>
+      <p>
+        The <b>first training-free, one-shot pruning framework</b> for Diffusion Models, supporting diverse architectures and pruning granularities. Uses Optimal Brain Surgeon (OBS) to achieve <b>SOTA</b> compression with high generative quality.
+      </p>
+    </td>
+  </tr>
+</table>
+
 OBS-Diff-SDXL provides a collection of structured-pruned checkpoints for the Stable Diffusion XL (SDXL) base model, compressed using the OBS-Diff framework. By leveraging an efficient one-shot pruning algorithm, this model significantly reduces the parameter count of the UNet while maintaining high-fidelity image generation capabilities. The provided variants cover a sparsity range from 10% to 30%, offering a trade-off between model size and performance.
 ![](sdxl1.png)
 ![](sdxl2.png)
@@ -62,4 +82,16 @@ image = pipe(
 
 image.save("output_pruned.png")
 
+```
+
+### Citation
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{zhu2025obs,
+  title={OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot},
+  author={Zhu, Junhan and Wang, Hesong and Su, Mingluo and Wang, Zefang and Wang, Huan},
+  journal={arXiv preprint arXiv:2510.06751},
+  year={2025}
+}
 ```