The video introduces diffusion models like Dolly and Stable Diffusion, which are used for generating images. The presenter aims to explain how these models work, comparing them to generative adversarial networks (GANs).

A quick recap of how GANs work is given. GANs use a generator network to create images from random noise and a discriminator network to distinguish between real and fake images. However, GANs can be difficult to train and suffer from issues like mode collapse.

Diffusion models simplify image generation by iteratively adding small amounts of noise to an image until it becomes pure noise. The goal is to train a network to reverse this process and remove noise.

The video discusses different schedules for adding noise, such as linear schedules or schedules that ramp up the amount of noise over time. These schedules determine how much noise is added at each step.

A U-Net shaped network is used to predict the noise added to an image at a specific time step. The network takes the noisy image and the time step as input and outputs an estimate of the noise. The goal is to predict the noise that, when removed, gets back to the original image.

The image generation process involves starting with random noise and iteratively removing predicted noise to gradually reveal an image. At each step, the network estimates the noise and subtracts it from the image, then adds back most, but not all, of the noise, before repeating the process.

To control the image generation process, the network is conditioned on text embeddings. This allows users to specify the content of the generated image, such as "frogs on stilts." Classifier free guidance further improves the quality of these models.

Classifier free guidance involves feeding the network the same image twice - once with text embeddings and once without. By comparing and amplifying the difference between the two outputs, the network is able to have improved results and generate more targeted images.

Stable Diffusion is mentioned as a freely available diffusion model that users can experiment with. The presenter has used Google Colab and eight pounds worth of Google premium access to generate images and plans to discuss the codebase in a future video.