Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import os
 import cv2
 import argparse
@@ -117,11 +116,9 @@ def load_img (filename, norm=True):
 
 def inference(image, upscale, large_input_flag, color_fix):
 	model = set_safmn(upscale)
-
-    img = np.array(image)
-    img = img.astype(np.float32) / 255.
+	
+    img = np.array(image).astype(np.float32) / 255.
     
-    # img2tensor
     y = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
     y = y.unsqueeze(0).to(device)
 
@@ -173,11 +170,8 @@ title = "SAFMN for Real-world SR"
 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.
 <br>
-
 ### If our work is useful for your research, please consider citing:
 <code>
 @inproceedings{sun2023safmn,