Update app.py

app.py

@@ -158,12 +158,11 @@ def inference(image, upscale, large_input_flag, color_fix):
 
 title = "SAFMN for Real-world SR (running on CPU)"
 description = ''' ### Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution - ICCV 2023
-#### [Long Sun](https://github.com/sunny2109), [Jiangxin Dong](https://scholar.google.com/citations?user=ruebFVEAAAAJ&hl=zh-CN&oi=ao), [Jinhui Tang](https://scholar.google.com/citations?user=ByBLlEwAAAAJ&hl=zh-CN), and [Jinshan Pan](https://jspan.github.io/)
-#### [IMAG Lab](https://imag-njust.net/), Nanjing University of Science and Technology
-#### Drag the slider on the super-resolution image left and right to see the changes in the image details.
-#### SAFMN performs x2/x4 upscaling on the input image.  If the input image is larger than 720P, it is recommended to use Memory-efficient inference.
-<br>
-#### If our work is useful for your research, please consider citing:
+### [Long Sun](https://github.com/sunny2109), [Jiangxin Dong](https://scholar.google.com/citations?user=ruebFVEAAAAJ&hl=zh-CN&oi=ao), [Jinhui Tang](https://scholar.google.com/citations?user=ByBLlEwAAAAJ&hl=zh-CN), and [Jinshan Pan](https://jspan.github.io/)
+### [IMAG Lab](https://imag-njust.net/), Nanjing University of Science and Technology
+### Drag the slider on the super-resolution image left and right to see the changes in the image details.
+### SAFMN performs x2/x4 upscaling on the input image.  If the input image is larger than 720P, it is recommended to use Memory-efficient inference.
+### If our work is useful for your research, please consider citing:
 <br>
 <code>
 @inproceedings{sun2023safmn,