Update app.py

app.py

@@ -127,7 +127,12 @@ with demo:
      However, VQ-VAEs latent space is **discrete** rather than continuous. Below, we can view how well this model compresses and reconstructs MNIST digits, but more importantly, we can see a
      discretized latent representation. These discrete representations can then be paired with a network like PixelCNN to generate novel images.
      
-     VQ-VAEs are one of the tools used by DALL-E and are some of the only models that perform on par with VAEs but with a discrete latent space.""")
+     VQ-VAEs are one of the tools used by DALL-E and are some of the only models that perform on par with VAEs but with a discrete latent space.
+     For more information check out this [paper](https://arxiv.org/abs/1711.00937) and
+     [example](https://keras.io/examples/generative/vq_vae/).
+     Full Credits for this example go to [Sayak Paul](https://twitter.com/RisingSayak).
+     Model card can be found [here](https://huggingface.co/brendenc/VQ-VAE)
+     Demo by [Brenden Connors](https://www.linkedin.com/in/brenden-connors-6a0512195)
 
     with gr.Row():
         with gr.Column():