Edge-Aware Image Manipulation via Diffusion Models with a Novel Structure-Preservation Loss

• URL: http://arxiv.org/abs/2601.16645v1
• Date: Fri, 23 Jan 2026 11:06:51 GMT
• Title: Edge-Aware Image Manipulation via Diffusion Models with a Novel Structure-Preservation Loss
• Authors: Minsu Gong, Nuri Ryu, Jungseul Ok, Sunghyun Cho,
• Abstract summary: We propose a novel Structure Preservation Loss (SPL) to quantify structural differences between input and edited images.<n>Our training-free approach integrates SPL directly into the diffusion model's generative process to ensure structural fidelity.<n> Experiments confirm SPL enhances structural fidelity, delivering state-of-the-art performance in latent-diffusion-based image editing.
• Score: 32.26030534230571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in image editing leverage latent diffusion models (LDMs) for versatile, text-prompt-driven edits across diverse tasks. Yet, maintaining pixel-level edge structures-crucial for tasks such as photorealistic style transfer or image tone adjustment-remains as a challenge for latent-diffusion-based editing. To overcome this limitation, we propose a novel Structure Preservation Loss (SPL) that leverages local linear models to quantify structural differences between input and edited images. Our training-free approach integrates SPL directly into the diffusion model's generative process to ensure structural fidelity. This core mechanism is complemented by a post-processing step to mitigate LDM decoding distortions, a masking strategy for precise edit localization, and a color preservation loss to preserve hues in unedited areas. Experiments confirm SPL enhances structural fidelity, delivering state-of-the-art performance in latent-diffusion-based image editing. Our code will be publicly released at https://github.com/gongms00/SPL.

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