Diffusion models are an essential part of modern machine learning and generative modeling. They are powerful tools for generating high-quality data, such as images, by gradually transforming random noise into meaningful content. These models have gained prominence for their ability to produce realistic and diverse outputs, making them a valuable asset in various AI applications, including image synthesis, denoising, and inpainting.

Here are two excellent tutorials by Outlier and Explained that provide a comprehensive introduction to diffusion models, explaining their underlying principles and demonstrating their practical applications. These tutorials are ideal for anyone looking to understand how diffusion models work and why they are becoming so important in the field of AI.

Tutorials One by Explained by ExplainingAI

Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models Code | DDPM Pytorch Implementation

Stable Diffusion from Scratch in PyTorch | Unconditional Latent Diffusion Models

Stable Diffusion from Scratch in PyTorch | Conditional Latent Diffusion Models

ControlNet with Diffusion Models | Explanation and PyTorch Implementation

Tutorials Two by Outlier

Diffusion Models | Paper Explanation | Math Explained

Diffusion Models | PyTorch Implementation