GG-PA composes diffusion priors with physical context via a derived Gibbs sampler that is asymptotically exact as diffusion time approaches zero and exact at finite times for quadratic interactions.
Generative modeling by estimating gradients of the data distribution.Advances in Neural Information Processing Systems, 32
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Composing diffusion priors with explicit physical context via generative Gibbs sampling
GG-PA composes diffusion priors with physical context via a derived Gibbs sampler that is asymptotically exact as diffusion time approaches zero and exact at finite times for quadratic interactions.