Introduction to Diffusion Models

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Diffusion models are a class of generative models that learn to reverse a gradual noising process. They have become the dominant approach for high-quality image generation, with notable examples including DALL-E, Stable Diffusion, and Imagen.

Key concepts include:

• Forward process: Gradually adding Gaussian noise to data over timesteps
• Reverse process: Learning to denoise and recover the original data
• Score matching: Estimating the gradient of the data distribution

For a comprehensive introduction, see Denoising Diffusion Probabilistic Models and What are Diffusion Models?.