Update app.py

app.py

@@ -71,8 +71,7 @@ def load_network(net, load_path, strict=True, param_key='params'):
     net.load_state_dict(load_net, strict=strict)
 
 CONFIG = "configs/SMFANet_plus_x4SR.yml"
-# hf_hub_download(repo_id="eduardzamfir/SeemoRe-T", filename="SeemoRe_T_X4.pth", local_dir="./")
-MODEL_NAME = "pth/SMFANet_DF2K_100w_x4SR.pth"
+MODEL_NAME = "pth/SMFANet_plus_DF2K_100w_x4SR.pth"
 
 # parse config file
 with open(os.path.join(CONFIG), "r") as f:
@@ -91,19 +90,11 @@ load_network(model, MODEL_NAME, strict=True, param_key='params')
 
 
 title = "See More Details"
-description = ''' ### See More Details: Efficient Image Super-Resolution by Experts Mining - ICML 2024, Vienna, Austria
+description = ''' ### [ECCV 2024] SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution 
 
-#### [Eduard Zamfir<sup>1</sup>](https://eduardzamfir.github.io), [Zongwei Wu<sup>1*</sup>](https://sites.google.com/view/zwwu/accueil), [Nancy Mehta<sup>1</sup>](https://scholar.google.com/citations?user=WwdYdlUAAAAJ&hl=en&oi=ao),  [Yulun Zhang<sup>2,3*</sup>](http://yulunzhang.com/) and [Radu Timofte<sup>1</sup>](https://www.informatik.uni-wuerzburg.de/computervision/)
+#### [Mingjun Zheng](https://github.com/Zheng-MJ), [Long Sun](https://github.com/sunny2109), [Jiangxin Dong](https://scholar.google.com/citations?user=ruebFVEAAAAJ&hl=zh-CN&oi=ao), and [Jinshan Pan](https://jspan.github.io/)
 
-#### **<sup>1</sup> University of Würzburg, Germany - <sup>2</sup> Shanghai Jiao Tong University, China - <sup>3</sup> ETH Zürich, Switzerland**
-#### **<sup>*</sup> Corresponding authors**
-
-<details>
-<summary> <b> Abstract</b> (click me to read)</summary>
-<p>
-Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden, hampering their practical utility. In response, we introduce **S**eemo**R**e, an efficient SR model employing expert mining. Our approach strategically incorporates experts at different levels, adopting a collaborative methodology. At the macro scale, our experts address rank-wise and spatial-wise informative features, providing a holistic understanding. Subsequently, the model delves into the subtleties of rank choice by leveraging a mixture of low-rank experts. By tapping into experts specialized in distinct key factors crucial for accurate SR, our model excels in uncovering intricate intra-feature details. This collaborative approach is reminiscent of the concept of **see more**, allowing our model to achieve an optimal performance with minimal computational costs in efficient settings
-</p>
-</details>
+#### [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. SeemoRe performs x4 upscaling on the input image.
@@ -111,19 +102,18 @@ Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses
 <br>
 
 <code>
-@inproceedings{zamfir2024details,
-  title={See More Details: Efficient Image Super-Resolution by Experts Mining}, 
-  author={Eduard Zamfir and Zongwei Wu and Nancy Mehta and Yulun Zhang and Radu Timofte},
-  booktitle={International Conference on Machine Learning},
-  year={2024},
-  organization={PMLR}
-}
+@inproceedings{smfanet,
+    title={SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution},
+    author={Zheng, Mingjun and Sun, Long and Dong, Jiangxin and Pan, Jinshan},
+    booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+    year={2024}
+ }
 </code>
 <br>
 '''
 
 
-article = "<p style='text-align: center'><a href='https://eduardzamfir.github.io/seemore' target='_blank'>See More Details: Efficient Image Super-Resolution by Experts Mining</a></p>"
+article = "<p style='text-align: center'><a href='https://raw.githubusercontent.com/Zheng-MJ/SMFANet' target='_blank'>SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution </a></p>"
 
 #### Image,Prompts examples
 examples = [