VAE Series: How to Compress Images to Free Up GPU Space(AI Painting Creation Intro Course 11)

Explore the power of Variational Autoencoders (VAE) in image compression, noise reduction, and applications in AI, NLP, and anomaly detection. Discover VAE's potential!

Welcome to the "AI Painting Creation Intro Course" Series

Table of Contents

In the previous sessions, we've already covered three key modules: Transformer, UNet, and Clip.

The last piece of the puzzle in the Stable Diffusion knowledge map is the VAE module, which we will discuss today.

In Stable Diffusion, all denoising and noise-adding processes don't occur directly in the image space.

The VAE module's role is to "compress" the image into a special space, where the "resolution" is lower than in the image space, making it easier to perform noise addition and removal.

Afterward, this special space can be easily "decompressed" back into the image space.

In this session, we’ll explore the basic principles of VAE.

Once we understand VAE, we'll have covered all the core modules of the Stable Diffusion model. We'll also use the VAE module when training our own Stable Diffusion models.