Enhance model card with metadata, introduction, usage, and citation

README.md

@@ -1,6 +1,47 @@
 ---
 license: apache-2.0
+pipeline_tag: text-to-image
+library_name: diffusers
 ---
+
+# InstanceAssemble
+> Official implementation of "InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention" (NeurIPS 2025).
+
+<p align="center">
+  <img src="https://github.com/FireRedTeam/InstanceAssemble/raw/main/fig/teaser.jpg" alt="Teaser of InstanceAssemble" width="800">
+</p>
+
 This repository contains the model used in [InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention](https://arxiv.org/abs/2509.16691).
 
-Code: https://github.com/FireRedTeam/InstanceAssemble.
+## Introduction
+
+InstanceAssemble is a lightweight framework for Layout-to-Image generation that enables precise spatial control. We also introduce DenseLayout and Layout Grounding Score (LGS) for rigorous evaluation, where InstanceAssemble achieves state-of-the-art performance on both sparse and dense layouts.
+
+For the official code and more details, please refer to the GitHub repository: [https://github.com/FireRedTeam/InstanceAssemble](https://github.com/FireRedTeam/InstanceAssemble).
+
+## Usage
+
+### Inference
+```bash
+# sd3 based
+python inference.py --model_type sd3 --input_json ./demo/bigchair.json
+# flux based
+python inference.py --model_type fluxdev --input_json ./demo/bigchair.json
+python inference.py --model_type fluxschnell --input_json ./demo/bigchair.json
+```
+
+### Streamlit demo
+```bash
+streamlit run demo.py
+```
+
+## Citation
+
+```
+@article{xiang2025instanceassemble,
+      title={InstanceAssemble: Layout-Aware Image Generation via Instance Assembling Attention}, 
+      author={Qiang Xiang and Shuang Sun and Binglei Li and Dejia Song and Huaxia Li and Nemo Chen and Xu Tang and Yao Hu and Junping Zhang},
+      journal={arXiv preprint arXiv:2509.16691},
+      year={2025},
+}
+```