Denoising diffusion probabilistic models: Generating images

Overview of generative models

Applications of generative models

Introducing GANs and diffusion models

Generator and discriminator

Architectural overview of a GAN

Training the generator and discriminator

Common problems with GANs

Getting set up with Google Colab

Loading the fashion MNIST data set

The generator network

The discriminator network

Adversary loss functions

Training the generative adversarial network

Generating images using the GAN

Overview of CNNs

Transposed convolutional layer

Deep Convolutional GANs

Greyscale images: Generator and discriminator in a Deep Convolutional GAN

Greyscale images: Training a Deep Convolutional GAN

Color images: Loading multichannel image data

Color images: Generator and discriminator in a Deep Convolutional GAN

Color images: Training a Deep Convolutional GAN

Generative learning trilemma

Introducing denoising diffusion probabilistic models

How do denoising diffusion probabilistic models work?

Forward diffusion process

Reverse diffusion process

Training a diffusion model: Intuition

Denoising diffusion probabilistic models: Exploring implementation on GitHub

Denoising diffusion probabilistic models: Code overview

Denoising diffusion probabilistic models: Code tweaks

Summary and next steps