Diffusion Models

STANDARD DEFINITION · CACHED

are a class of based on that learn to generate data by reversing a gradual noise process. The framework typically consists of a forward pass, where Gaussian noise is incrementally added to a signal until it becomes pure white noise, and a reverse pass, where a —often utilizing a architecture—is trained to predict and remove that noise. These models have largely superseded (GANs) due to their training stability and high sample quality, becoming the backbone for state-of-the-art systems like by , by , and by . Mathematically, they are often formulated as (DDPM) or via , relying heavily on and to sample from complex data distributions.

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Diffusion Models

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