How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile

Introduction to Diffusion Models

In this section, the speaker introduces diffusion models and explains how they differ from generative adversarial networks (GANs).

Generative Adversarial Networks (GANs)

• GANs are a standard way of generating images using deep neural networks.

• They use a large generator network to produce an image based on random noise.

• Another network is used to determine if the generated image is real or fake.

• However, GANs can be difficult to train and may suffer from problems like mode collapse.

Diffusion Models

• Diffusion models simplify the process of generating images by breaking it down into iterative steps.

• The process involves adding noise to an image and gradually removing it through multiple iterations.

• The goal is to create a training algorithm that can undo this process and generate high-quality images.

Understanding Diffusion Models

In this section, the speaker provides a more detailed explanation of how diffusion models work.

Adding Noise

• An original image is selected, such as an image of a rabbit.

• Gaussian noise is added to the image, creating a slightly noisier version of the rabbit.

• More noise is added in subsequent iterations until only noise remains.

Training Algorithm

• A training algorithm must be created that can reverse this process and generate high-quality images from noisy ones.

• Adding too much noise makes this task difficult, so finding the right amount of noise is important.

Conclusion

In this section, the speaker concludes their discussion on diffusion models.

Advantages of Diffusion Models

• Diffusion models offer several advantages over GANs, including simpler training algorithms and fewer problems with mode collapse.

• They also allow for greater control over the generation process by adjusting the amount of noise added to an image.

Potential Applications

• Diffusion models have potential applications in a variety of fields, including computer vision and natural language processing.

• They could be used to generate realistic images for video games or create more accurate language models for text-based applications.

Introduction to Noise Removal Techniques

In this section, the speaker introduces noise removal techniques and explains how they work.

How Noise Removal Techniques Work

• The goal of noise removal techniques is to train a network to undo the process of adding noise to an image.

• There are different strategies for adding noise, such as a linear schedule or ramping up the amount of noise added later.

• A schedule is used to determine how much noise is added at each time step, with varying amounts between 1 and T.

• To remove noise from an image, a giant unit-shaped network is used along with an encoder-decoder network. The time step is also included in the input so that the network knows how much noise to remove.

Predicting Noise in Images

In this section, the speaker discusses predicting noise in images and how it can be used for removing noise.

Predicting Noise in Images

• The goal is to predict all the noise that was added to an image so that it can be removed and the original image can be recovered.

• Instead of predicting the exact time steps used in training, a method can be used where all the noise in an image is predicted regardless of time step. This allows for faster inference times.

Image Generation with Noise

In this section, the speaker explains how to use a network to generate images by removing noise from a noisy image.

Generating Images by Removing Noise

• The original image is taken and noise is added to it.

• A network is used to predict what the noise was in the original image.

• The predicted noise is subtracted from the noisy image to get an estimate of what the original image was.

• This process is repeated multiple times, each time adding back some of the noise until we end up with an image that looks like the original.

Guiding Image Generation

• To guide this process, we can condition the network on text input as well.

• We start with a random noise image and pass it through the network while also providing text input.

• The output will be a hand-drawn image based on both the random noise and text input.

Producing Images with Text Embeddings

In this section, the speaker explains how to produce images using text embeddings. They describe a process that involves subtracting noise from an image and then adding it back in repeatedly while also adding text embeddings to improve the output.

The Process of Producing Images with Text Embeddings

• To produce an image with text embeddings, you start by subtracting noise from an image.

• You then add back some noise to get a slightly less noisy version of the original image.

• This process is repeated multiple times while also adding text embeddings to improve the output.

• A final trick called classifier-free guidance is used to make the output even more tied to the text. This involves putting in two images - one with text embedding and one without - and amplifying the difference between them.

Classifier-Free Guidance

In this section, the speaker explains what classifier-free guidance is and how it works.

What is Classifier-Free Guidance?

• Classifier-free guidance is a technique used in producing images with text embeddings.

• It involves calculating the difference between two predictions - one where no information on what was in the image was given, and another where a version of a network was given information on what was in the image.

• The difference between these two predictions is amplified when looping through this process, which helps target specific outputs.

Playing With Neural Networks

In this section, the speaker discusses whether people can play around with neural networks without going to websites or typing words.

Can People Play Around With Neural Networks?

• It is possible to play around with neural networks without going to websites or typing words.

• There are free tools available, such as Stable Diffusion, that can be used through Google Colab.

• The code for running these networks is relatively easy to use and can produce images with just one function call.