Understanding Score-Based Generative Models

A gentle introduction to score-based diffusion models and their applications in generative AI.

Introduction

Score-based generative models are a class of probabilistic models that use the gradient (score) of the data distribution to generate new samples. They have become popular for their ability to generate high-quality images and other data types.

How They Work

These models learn to estimate the score function of the data and use stochastic differential equations (SDEs) to sample from the learned distribution. This approach is closely related to diffusion models and has led to state-of-the-art results in generative modeling.

Applications

• Image synthesis
• Audio generation
• Text-to-image models

For more details, see the original papers and recent tutorials on score-based generative modeling.