Update app.py

app.py

@@ -60,18 +60,14 @@ def inference(LR, Ref):
     return "result/0000.png"
 
 title="RefVSR"
-#description="Demo application for Reference-based Video Super-Resolution (RefVSR).\nInstruction: Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively.\nNote 1: This demo only supports RefVSR for a single LR and Ref frame due to computational complexity. Hence, the model might not take advantage of temporal frames. \nNote 2: The model is our small 8K model trained with the proposed two-stage training strategy. \nNote 3: The spatial size of input LR and Ref frames is 1920x1080 (HD), in the PNG format."
 description="Demo application for Reference-based Video Super-Resolution (RefVSR). Upload a low-resolution frame and a reference frame to 'LR' and 'Ref' input windows, respectively."
 
-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 frames will be resized so that the length of a longer side of the frames doesn't exceed 256 pixels.</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>"
+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')
-#Image.open('LR.png').save(os.path.join(LR_path, '0000.png'))
-#Image.open('LR.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)