Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models

• URL: http://arxiv.org/abs/2605.31162v1
• Date: Fri, 29 May 2026 11:11:04 GMT
• Title: Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models
• Abstract summary: h-space patching, the dominant paradigm for training-free diffusion editing, systematically fails for global, low-level transformations.<n>We introduce a novel, generalized framework for image-editing in unconditional diffusion models without explicit training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We show that h-space patching, the dominant paradigm for training-free diffusion editing, systematically fails for global, low-level transformations required for aesthetic and perceptual refinement. We introduce a novel, generalized framework for image-editing in unconditional diffusion models without explicit training. This inference-time mechanism operates on low-level features by extracting degradation concept vectors and combining bottleneck patching with classifier-free guidance to guide sampling away from the degraded manifold, producing consistently improved images without any model retraining.

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