Inference Strategies for Diffusion Models
Explore inference strategies for diffusion models to generate images faster by reducing the number of denoising steps without retraining. Understand how DDIM uses deterministic step skipping, and DPM-Solver applies numerical methods to balance speed with image quality, helping you optimize model deployment based on application needs.
We'll cover the following...
Diffusion models generate images by gradually transforming noise into structure. At inference time, the model starts with a completely noisy image and repeatedly applies a denoising network to remove noise step by step. Each step produces a slightly cleaner image than the previous one.
This process works well, but it is inherently slow.
What happens during diffusion inference
Consider a diffusion model trained with 1,000 time steps. During training, the model learns how to reverse each small noise addition. At inference time, the simplest approach is to reverse this process one step at a time:
Each of these steps requires a forward pass through a large neural network. If a single forward pass takes, for example, 20 ...