Now, we can extend our VAE architecture by **adding another input** to the decoder and encoder.    
We will pass a **class label** in one-hot encoded format to let the model know which particular digit we are generating.    
Here is how the additional input will look like:

As a result, the input will consist of pixels of each particular sample and a one-hot encoded label that will determine the class of this sample.

>Note
>
>You can find the source code via the following <a 
href="https://colab.research.google.com/drive/1Kv9johbUMJfKQHPxJAb6s-gdF1sMeFZQ?usp=sharing"
target="_blank"
style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Link</a>. If you want to run the code or even change some components, you can copy the notebook and work with the copy.

We can see that the generated images have good quality, and finally, we can control what we generate.    
However, we have to take into account that the MNIST dataset is quite simple. If we want to generate more complex data, we have to consider more advanced architectures like GANs or their variations.

Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

In this section, we'll start by understanding different types of generative models. We'll look into autoencoders, which transform data, GANs, which use a competitive approach, and diffusion networks, which spread out data. You'll learn how each model generates data differently, paving the way for further exploration in generative networks.

We will implement a simple Variational Autoencoder (VAE) using the MNIST dataset. We will build and train the VAE model, which includes designing the encoder and decoder networks, sampling from the latent space using the reparameterization trick, and optimizing the combined reconstruction and Kullback-Leibler divergence loss.

Now we will explore the generator-discriminator principle fundamental to GANs, implement a simple GAN model, and examine various GAN variations. Additionally, we will provide an overview of some existing state-of-the-art image generation models.

Image Synthesis Through Generative Networks

CVAE Implementation