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Consistency Models Improve Diffusion Inverse Solvers
March 20, 2024, 4:42 a.m. | Tongda Xu, Ziran Zhu, Dailan He, Yuanyuan Wang, Ming Sun, Ning Li, Hongwei Qin, Yan Wang, Jingjing Liu, Ya-Qin Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: Diffusion inverse solvers (DIS) aim to find an image $x$ that lives on the diffusion prior while satisfying the constraint $f(x) = y$, given an operator $f(.)$ and measurement $y$. Most non-linear DIS use posterior mean $\hat{x}_{0|t}=\mathbb{E}[x_0|x_t]$ to evaluate $f(.)$ and minimize the distance $||f(\hat{x}_{0|t})-y||^2$. Previous works show that posterior mean-based distance is biased; instead, posterior sample $x_{0|t}\sim p_{\theta}(x_0|x_t)$ promises a better candidate. In this paper, we first clarify when is posterior sample better: $1)$ …
abstract aim arxiv cs.cv cs.lg diffusion image linear mean measurement non-linear posterior prior show type
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