Guess & Guide: Gradient-Free Zero-Shot Diffusion Guidance

• URL: http://arxiv.org/abs/2603.07860v1
• Date: Mon, 09 Mar 2026 00:21:53 GMT
• Title: Guess & Guide: Gradient-Free Zero-Shot Diffusion Guidance
• Abstract summary: We introduce a lightweight likelihood surrogate that eliminates the need to calculate gradients through the denoiser network.<n>Experiments confirm that using our method, the inference cost drops dramatically.<n>At the same time, our approach delivers the highest results in multiple tasks.
• Score: 29.644504577943696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pretrained diffusion models serve as effective priors for Bayesian inverse problems. These priors enable zero-shot generation by sampling from the conditional distribution, which avoids the need for task-specific retraining. However, a major limitation of existing methods is their reliance on surrogate likelihoods that require vector-Jacobian products at each denoising step, creating a substantial computational burden. To address this, we introduce a lightweight likelihood surrogate that eliminates the need to calculate gradients through the denoiser network. This enables us to handle diverse inverse problems without backpropagation overhead. Experiments confirm that using our method, the inference cost drops dramatically. At the same time, our approach delivers the highest results in multiple tasks. Broadly speaking, we propose the fastest and Pareto optimal method for Bayesian inverse problems.

Related papers

• Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching [66.39914384073145]
  We propose a self-consistency framework that turns cheap diffusion-sampled reasoning into a reusable pool of step-level candidates.<n>We find that step-level recombination is most beneficial on harder problems.<n>Our training-free framework improves average accuracy by up to 2 across six math and coding tasks.
  arXiv  Detail & Related papers  (2026-02-26T11:08:39Z)
• Self-diffusion for Solving Inverse Problems [3.8870795921263728]
  We propose self-diffusion, a novel framework for solving inverse problems without relying on pretrained generative models.<n>Self-diffusion exploits the spectral bias of neural networks and modulates it through a scheduled noise process.<n>We demonstrate the effectiveness of our approach on a variety of linear inverse problems, showing that self-diffusion achieves competitive or superior performance compared to other methods.
  arXiv  Detail & Related papers  (2025-10-24T12:57:22Z)
• GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver [120.67680383929081]
  We introduce a simple parameterization of the ODE sampler that does not require additional training tricks and improves quality over existing approaches.<n>We further combine the original distillation loss with adversarial training, which mitigates artifacts and enhances detail fidelity.
  arXiv  Detail & Related papers  (2025-10-20T16:14:38Z)
• ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems [10.698572109242434]
  We devise a simple wrapper, ReGuidance, for boosting both the sample realism and reward achieved by training-free methods.<n>We evaluate our wrapper on hard inverse problems like large box in-painting and super-resolution with high upscaling.
  arXiv  Detail & Related papers  (2025-06-12T17:55:17Z)
• Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems [12.482127049881026]
  We propose a novel approach to solve inverse problems with a diffusion prior from an amortized variational inference perspective. Our amortized inference learns a function that directly maps measurements to the implicit posterior distributions of corresponding clean data, enabling a single-step posterior sampling even for unseen measurements.
  arXiv  Detail & Related papers  (2024-07-23T02:14:18Z)
• Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing [84.97865583302244]
  We propose a new method called Decoupled Annealing Posterior Sampling (DAPS)<n>DAPS relies on a novel noise annealing process.<n>We demonstrate that DAPS significantly improves sample quality and stability across multiple image restoration tasks.
  arXiv  Detail & Related papers  (2024-07-01T17:59:23Z)
• Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems [26.134240531687453]
  The ProjDiff algorithm harnesses the prior information and the denoising capability of a pre-trained diffusion model within the optimization framework.<n>Experiments on the image restoration tasks and source separation and partial generation tasks demonstrate that ProjDiff exhibits superior performance across various linear and nonlinear inverse problems.
  arXiv  Detail & Related papers  (2024-06-11T05:35:18Z)
• Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance [52.093434664236014]
  Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. Inspired by this finding, we propose to improve recent methods by using more principled covariance determined by maximum likelihood estimation.
  arXiv  Detail & Related papers  (2024-02-03T13:35:39Z)
• Diffusion Posterior Sampling for General Noisy Inverse Problems [50.873313752797124]
  We extend diffusion solvers to handle noisy (non)linear inverse problems via approximation of the posterior sampling. Our method demonstrates that diffusion models can incorporate various measurement noise statistics.
  arXiv  Detail & Related papers  (2022-09-29T11:12:27Z)
• Improving Diffusion Models for Inverse Problems using Manifold Constraints [55.91148172752894]
  We show that current solvers throw the sample path off the data manifold, and hence the error accumulates. To address this, we propose an additional correction term inspired by the manifold constraint. We show that our method is superior to the previous methods both theoretically and empirically.
  arXiv  Detail & Related papers  (2022-06-02T09:06:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.