Fast Diffusion Sampler
Chapter 20: ODE, SDE, and Continuous Limits of Algorithms — DDIM and Fast Diffusion Samplers
From the book
Chapter 20: ODE, SDE, and Continuous Limits of Algorithms. In the chapter mind map this icon labels DDIM and Fast Diffusion Samplers. The discussion below is excerpted and lightly edited from § DDIM and Fast Diffusion Samplers in Mathematics for AI and Machine Learning.
DDIM (Denoising Diffusion Implicit Models) was one of the first methods to demonstrate that diffusion models can be sampled deterministically. DDIM uses the learned denoising function without explicit noise injection, effectively solving a discretized version of the probability flow ODE.
The key insight is that the denoising function learned during training already encodes the score function, so we can use it deterministically during sampling.
What this drawing shows
What you see. Dense gray $N$-step chain fixed; a purple few-step fast-sampler path extends in far fewer jumps toward the same endpoint.
In the mind map. Chapter 20 — DDIM and Fast Diffusion Samplers. See From the book above for definitions, figures, and worked examples.
Where to read next
Read the full definitions, figures, and worked examples in Chapter 20: ODE, SDE, and Continuous Limits of Algorithms — see the mind-map node DDIM and Fast Diffusion Samplers.