Generative Models

Diffusion Models: A New Paradigm for AI Image Generation

What are Diffusion Models?

Diffusion Models are a class of powerful generative models that create high-quality images from random noise through a gradual denoising process.

Core Concepts

Diffusion models involve two key processes:

1. Forward Diffusion Process (Adding Noise)

Gradually add Gaussian noise to data until it becomes pure noise:

$$ x_t = \sqrt{\alpha_t} x_0 + \sqrt{1-\alpha_t} \epsilon $$

2. Reverse Denoising Process (Generation)

Train a neural network to learn the reverse process, recovering data from noise: