first commit

README.md

@@ -90,4 +90,17 @@ IPython.display.Audio(data=audio, rate=16000)
 
 The auffusion model will be automatically downloaded from huggingface and saved in cache. Subsequent runs will load the model directly from cache.
 
-Other audio manipulation examples can be seen in [https://github.com/happylittlecat2333/Auffusion/notebooks](https://github.com/happylittlecat2333/Auffusion/notebooks). We only show the default text-to-audio example here.
+Other audio manipulation examples can be seen in [https://github.com/happylittlecat2333/Auffusion/notebooks](https://github.com/happylittlecat2333/Auffusion/notebooks). We only show the default text-to-audio example here.
+
+##  Citation
+
+Please consider citing the following article if you found our work useful:
+
+```bibtex
+@article{xue2024auffusion,
+  title={Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation}, 
+  author={Jinlong Xue and Yayue Deng and Yingming Gao and Ya Li},
+  journal={arXiv preprint arXiv:2401.01044},
+  year={2024}
+}
+```