Inference Strategies for Diffusion Models
Explore inference strategies for diffusion models that enable faster image generation by reducing denoising steps. Understand how DDIM uses deterministic paths to skip steps and how DPM-Solver applies numerical methods for efficient denoising. This lesson helps you balance speed and quality in diffusion model outputs for practical applications.
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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 ...
Each of these steps requires a forward pass through a large neural network. If a single forward pass takes, for example, 20 ...