notebook link

app.py

@@ -77,7 +77,7 @@ def generate(base_image,style_image,epochs,progress=gr.Progress()):
   return generated_image
 
 title = "Neural Style Transfer Demo"
-description = "This is my implementation of the neural style transfer algorithm using Tensorflow2.To use this app, select a real photo as a content image and an art piece as style image from an URL or from your PC, set the number of epochs (it is recommended to leave the default value), and run the app. THIS MAY TAKE SOME TIME (AROUND 5 MINUTES WITH THE DEFAULT VALUE), PLEASE BE PATIENT (╯°□°)╯."
+description = "This is my implementation of the neural style transfer algorithm using Tensorflow2.To use this app, select a real photo as a content image and an art piece as style image from an URL or from your PC, set the number of epochs (it is recommended to leave the default value), and run the app. THIS MAY TAKE SOME TIME (AROUND 5 MINUTES WITH THE DEFAULT VALUE), PLEASE BE PATIENT (╯°□°)╯. If you want better and faster results, you can try running the code on Google Colab with a GPU. You can run 4000 epochs in 8 minutes ± 2. You can modify this [notebook](https://colab.research.google.com/drive/1KWqQmz_nM3tckiRm5ES_Y5YHl97aILmo?usp=sharing) for that purpose. It is the same implementation that is used in the app."
 article = "The NST algorithm is an algorithm that allows you to replicate an image A with similar features to the ones present in an image B. In a nutshell, this is done by using a pretrained CNN to  perform gradient descent on the weighted cost of a style and content cost function, which correspond to the frobenius norm across the features’ cross covariance across different layers and the simple norm respectively. The result of the loss is applied to a random generated image to get the hybrid."