How Multi-Head Attention Transforms AI: The Crucial Details(AI Painting Creation Intro Course 7)

Explore how Transformers and Multi-Head Attention enhance AI models like GPT, with insights into Self-Attention, LSTM, and efficient sequence processing.

Welcome to the "AI Painting Creation Intro Course" Series

Table of Contents

In the first two sessions, we learned about the noise-adding and denoising process in diffusion models and explored the algorithm behind the UNet model for noise prediction.

In fact, the Stable Diffusion model integrates a Transformer structure into the original UNet model. (We’ll understand how this is done after we learn about the UNet structure in the next session.) This approach offers dual benefits: The transformer not only enhances noise removal but also plays a key role in using prompts to control image content.

More importantly, the Transformer structure is also a core component of the GPT series.

In other words, truly understanding Transformers means stepping into the world of modern AIGC.

In this session, I’ll reveal the algorithm behind Transformers.