Related papers: Amortized Posterior Sampling with Diffusion Prior Distillation

Amortized Posterior Sampling with Diffusion Prior Distillation

URL: http://arxiv.org/abs/2407.17907v1
Date: Thu, 25 Jul 2024 09:53:12 GMT
Title: Amortized Posterior Sampling with Diffusion Prior Distillation
Authors: Abbas Mammadov, Hyungjin Chung, Jong Chul Ye,
Abstract summary: We propose a variational inference approach to sample from the posterior distribution for solving inverse problems. We show that our method is applicable to standard signals in Euclidean space, as well as signals on manifold.
Score: 55.03585818289934
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We propose a variational inference approach to sample from the posterior distribution for solving inverse problems. From a pre-trained diffusion model, our approach trains a conditional flow model to minimize the divergence between the proposal variational distribution and the posterior distribution implicitly defined through the diffusion model. Once trained, the flow model is capable of sampling from the posterior distribution with a single NFE, amortized with respect to the measurement. The proposed method paves a new path for distilling a diffusion prior for efficient posterior sampling. We show that our method is applicable to standard signals in Euclidean space, as well as signals on manifold.

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