Denoising Diffusion Probabilistic Models (DDPM)

Exploring the core ideas behind DDPMs and their impact on image synthesis.

What is DDPM?

DDPMs are a type of generative model that iteratively denoise data, starting from pure noise, to generate realistic samples. They have set new benchmarks in image generation quality.

Key Concepts

• Forward and reverse diffusion processes
• Score matching
• Sampling with neural networks

DDPMs are widely used in modern generative AI systems, including text-to-image models.