URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.
Guidance with spherical gaussian constraint for condi- tional diffusion.arXiv preprint arXiv:2402.03201
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Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
FlowLPS perturbs flow-model estimates with Langevin steps then applies proximal refinement to balance fidelity and perceptual quality on linear inverse problems.
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
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Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.
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Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models
Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
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FlowLPS: Langevin-Proximal Sampling for Flow-based Inverse Problem Solvers
FlowLPS perturbs flow-model estimates with Langevin steps then applies proximal refinement to balance fidelity and perceptual quality on linear inverse problems.
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Saving Foundation Flow-Matching Priors for Inverse Problems
FMPlug adapts foundation flow-matching models into practical priors for inverse problems by combining instance-guided warm-start with sharp Gaussianity regularization, showing superior results on image restoration and scientific tasks with limited samples.
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A Survey on Diffusion Models for Inverse Problems
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.