Multi-History-Step SDE Inversion for Image Editing with Superior Regional Awareness

In recent years, diffusion stochastic differential equation (SDE)inversion and inversion-free methods have become prevalent for training-free image editing, as they can achieve faithful reconstruction withouttuning. However, existing approaches remain inefficient, exhibit limitedplasticity, and struggle to accurately preserve unedited regions. To ad-dress these issues, we propose MIEdit, a training-free editing frame-work based on SDE inversion. MIEdit introduces a predictor–correctormulti-history-step scheme to achieve superior editing quality with fewersteps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improv-ing stability and editing plasticity under large edits. MIEdit also includesInversion-Time Automatic Semantic Angle Masking (IASM); it leveragesclassifier-free guidance to automatically generate semantic angle masksduring inversion and applies them throughout the sampling process forregional constraints, without extra user inputs. We additionally constructEditEval++ (30 fine-grained tasks, 1,000+ image–text–mask triplets) forcomprehensive evaluation; experiments show that MIEdit outperformsstate-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.