Create README.md

README.md

@@ -1,13 +1,24 @@
----
-title: Composable Diffusion
-emoji: 🐠
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.3
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# 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)
+
+## 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: 
+
+[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**
+
+## Citation
+
+If you find our paper useful in your research, please cite the following paper:
+
+``` latex
+@article{liu2022compositional,
+  title={Compositional Visual Generation with Composable Diffusion Models},
+  author={Liu, Nan and Li, Shuang and Du, Yilun and Torralba, Antonio and Tenenbaum, Joshua B},
+  journal={arXiv preprint arXiv:2206.01714},
+  year={2022}
+}
+```