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README.md

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 # Composable Diffusion
 **Compositional Visual Generation with Composable Diffusion Models (ECCV 2022)**
 
-[Webpage](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | [GitHub](https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch)
+**[Webpage](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | [GitHub](https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch)**
 
 ## Overview
-We propose to use **conjunction and negation** (negative prompts) operators for **compositional generation with conditional diffusion models in test time without any training**. For more details, please refer to our paper: 
+We propose to use **conjunction and negation** (negative prompts) operators for **compositional generation with conditional diffusion models in test time without any training**. 
 
-[Compositional Visual Generation with Composable Diffusion Models](https://arxiv.org/abs/2206.01714)
-[Nan Liu](https://nanliu.io)*\*, [Shuang Li](https://people.csail.mit.edu/lishuang)*\*, [Yilun Du](https://yilundu.github.io)*\*, [Antonio Torralba](https://groups.csail.mit.edu/vision/torralbalab/), [Joshua B. Tenenbaum](https://mitibmwatsonailab.mit.edu/people/joshua-tenenbaum/), **ECCV 2022**
+For more details, please refer to our paper: 
+
+[Compositional Visual Generation with Composable Diffusion Models](https://arxiv.org/abs/2206.01714).<br>
+[Nan Liu](https://nanliu.io)*\, [Shuang Li](https://people.csail.mit.edu/lishuang)*\, [Yilun Du](https://yilundu.github.io)*\, [Antonio Torralba](https://groups.csail.mit.edu/vision/torralbalab/), [Joshua B. Tenenbaum](https://mitibmwatsonailab.mit.edu/people/joshua-tenenbaum/), **ECCV 2022**
 
 ## Citation