Update README.md

README.md

@@ -19,13 +19,44 @@ pipeline_tag: text-to-image
 ---
 
 
-This repository contains the structured pruned checkpoints for Stable Diffusion 3.5 Large. These models were compressed using OBS-Diff, an accurate one-shot pruning method designed to reduce model size and accelerate inference while preserving high-quality image generation capabilities.
 
-By removing redundant parameters from the Transformer backbone, we offer variants with different sparsity levels (15% - 30%), allowing for a flexible trade-off between efficiency and performance.
 
 # OBS-Diff Structured Pruning for Stable Diffusion 3.5-Large
 
+<div style="
+    display: flex; 
+    flex-wrap: wrap; 
+    align-items: flex-start; 
+    gap: 20px; 
+    border: 1px solid #e0e0e0; 
+    padding: 20px; 
+    border-radius: 10px; 
+    margin-bottom: 20px; 
+    background-color: #fff;
+">
+  
+  <div style="flex: 1; min-width: 280px; max-width: 100%;">
+    <img src="teaser.jpg" alt="OBS-Diff" style="width: 100%; height: auto; border-radius: 5px;" />
+  </div>
+
+  <div style="flex: 2; min-width: 300px;">
+    <h4 style="margin-top: 0;">✂️ <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>
+  </div>
+
+</div>
+
+This repository contains the structured pruned checkpoints for Stable Diffusion 3.5 Large. These models were compressed using OBS-Diff, an accurate one-shot pruning method designed to reduce model size and accelerate inference while preserving high-quality image generation capabilities.
 
+By removing redundant parameters from the Transformer backbone, we offer variants with different sparsity levels (15% - 30%), allowing for a flexible trade-off between efficiency and performance.
 ![](sd3-5-2.png)
 ![](sd3-5-1.png)
 ![](sd3-5-3.png)
@@ -74,4 +105,16 @@ 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}
+}
 ```