**Diffusion models** are a class of generative models used to create data by modeling the process of **gradually transforming simple noise** into complex, structured data.

## Key Concepts

The key concepts of diffusion models include:

### Forward diffusion process
In the forward process, data (like an image) is gradually corrupted by **adding noise** over several steps until it becomes almost indistinguishable from pure noise. This process usually has a specific design, where each step only depends on the immediate previous step.

### Reverse diffusion process
The reverse process aims to **undo the forward diffusion**, starting from pure noise and gradually removing the noise to reconstruct the original data. The goal is to model the reverse transitions accurately so that starting from a noise sample can eventually **produce a sample from the original data** distribution.

### Training objective
The model learns the **reverse process** by training on the corrupted data. It tries to predict the original data from the noisy data at each step. Typically, the model minimizes a loss function that measures the difference between the predicted data and the actual original data at each step.


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

Diffusion Models

Key Concepts

Forward diffusion process

Reverse diffusion process

Training objective

Match generative network types with their descriptions