Update app.py

app.py

@@ -28,7 +28,7 @@ os.system("wget https://www.dropbox.com/s/xv6inxwy0so4ni0/LR.png -O LR.png")
 os.system("wget https://www.dropbox.com/s/abydd1oczs1163l/Ref.png -O Ref.png")
 
 def resize(img):
-    max_side = 1024
+    max_side = 512
     w = img.size[0]
     h = img.size[1]
     if max(h, w) > max_side:
@@ -64,10 +64,9 @@ description="Demo application for Reference-based Video Super-Resolution (RefVSR
 
 article = "<p style='text-align: center'><b>To check the full capability of the module, we recommend to clone Github repository and run RefVSR models on videos using GPUs.</b></p><p style='text-align: center'>This demo runs on CPUs and only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model will not take advantage of temporal LR and Ref frames.</p><p style='text-align: center'>The model is the small-sized model trained with the proposed two-stage training strategy.</p><p style='text-align: center'>The sample frames are in HD resolution (1920x1080) and in the PNG format. </p><p style='text-align: center'><a href='https://junyonglee.me/projects/RefVSR' target='_blank'>Project</a> | <a href='https://arxiv.org/abs/2203.14537' target='_blank'>arXiv</a> | <a href='https://github.com/codeslake/RefVSR' target='_blank'>Github</a></p>"
 
-#LR = resize(Image.open('LR.png'))
-#Ref = resize(Image.open('Ref.png'))
-#LR.save('LR.png')
-#Ref.save('Ref.png')
+LR = resize(Image.open('LR.png')).save('LR.png')
+Ref = resize(Image.open('Ref.png')).save('Ref.png')
+
 examples=[['LR.png', 'Ref.png']]
 
 gr.Interface(inference,[gr.inputs.Image(type="pil"), gr.inputs.Image(type="pil")],gr.outputs.Image(type="file"),title=title,description=description,article=article,theme ="peach",examples=examples).launch(enable_queue=True)