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The Breakthrough Behind Modern AI Image Generators | Diffusion Models Part 1

A 24-minute video by Depth First about algebra and geometry, computing mechanisms and probability and information, on the AI shelf. It was posted to r/StableDiffusion as “The best tutorial on Diffusion I have seen so far”. It is pitched at intermediate level.

Depth First, Video, 24 min, 2024, on the AI shelf

The Breakthrough Behind Modern AI Image Generators | Diffusion Models Part 1

Study notes

Intermediate, Visual intuition. Step 10 of 11 in From probability to modern AI

Helpful first

• Basic probability
• Neural-network basics

What it builds

• Generate images by refining Gaussian noise.
• Locate images and noise in a shared space.
• Separate training from the generation process.

Useful moments

• 0:30Starting from Gaussian noise
• 4:10Images and noise in the same space

Check yourself: What makes removing noise repeatedly different from applying one ordinary image filter?

Next in the path: More Than Image Generators: A Science of Problem-Solving using Probability | Diffusion Models

Notes are based on metadata, Reddit context and selected English caption passages. They are not a review of every claim in the video.