🤖 AI Summary
Existing diffusion-based image editing methods lack a unified theoretical framework that jointly addresses core criteria such as controllability, fidelity to user intent, semantic consistency, locality, and perceptual quality. This work formulates diffusion editing as a guided transport process on the image manifold and introduces, for the first time, a task-agnostic evaluation framework. By leveraging conditional inverse-time generative operators, noise dynamics analysis, mask-guided local modeling, and Lipschitz error propagation theory, the study systematically characterizes the trade-offs among multiple performance dimensions across different editing paradigms. Theoretical analysis reveals bounds on the influence of guidance strength and inversion error on non-target regions, as well as the error accumulation mechanism in multi-step editing. Empirical benchmarking further demonstrates significant disparities among state-of-the-art methods in instruction following, region preservation, and semantic stability.
📝 Abstract
Diffusion-based editing has rapidly evolved from curated inpainting tools into general-purpose editors spanning text-guided instruction following, mask-localized edits, drag-based geometric manipulation, exemplar transfer, and training-free composition systems. Despite strong empirical progress, the field lacks a unified treatment of core desiderata that govern practical usability: controllability (how precisely and continuously the user can specify an edit), faithfulness to user intent (semantic alignment to instructions), semantic consistency (preservation of identity and non-target content), locality (containment of changes), and perceptual quality (artifact suppression and detail retention). This paper provides a theoretical and empirical analysis of general diffusion-based image editing, connecting diverse paradigms through a common view of editing as guided transport on a learned image manifold. We first formalize editing as an operator induced by a conditional reverse-time generative process and define task-agnostic metrics capturing instruction adherence, region preservation, semantic consistency, and stability under repeated edits. We then develop theory describing edit dynamics under (i) noise-injection and denoising transport, (ii) inversion-and-edit pipelines and the propagation of inversion errors, and (iii) locality constraints implemented via masked guidance or hard constraints. Under mild Lipschitz assumptions on the learned score or flow field, we derive bounds connecting guidance strength and inversion error to measurable deviations in non-target regions, and we characterize accumulation effects under iterative multi-turn editing. Empirically, we benchmark representative paradigms.