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

arXiv:2609.06602v1 Announce Type: new Abstract: 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 without tuning. However, existing approaches remain inefficient, exhibit limited plasticity, and struggle to accurately preserve unedited regions. To address these issues, we propose MIEdit, a training-free editing framework based on SDE inversion. MIEdit introduces a predictor-corrector multi-history-step scheme to achieve superior editing quality with fewer steps. We further mitigate heterogeneity and conflict between the multi-conditioned noise residuals and gradient terms during sampling, improving stability and editing plasticity under large edits. MIEdit also includes Inversion-Time Automatic Semantic Angle Masking (IASM); it leverages classifier-free guidance to automatically generate semantic angle masks during inversion and applies them throughout the sampling process for regional constraints, without extra user inputs. We additionally construct EditEval++ (30 fine-grained tasks, 1,000+ image-text-mask triplets) for comprehensive evaluation; experiments show that MIEdit outperforms state-of-the-art techniques. Project page: https://whywwwzzzg.github.io/MIEdit/.

Related

• DirectEdit: Step-Level Accurate Inversion for Flow-Based Image Editing
• DuET: Dual Expert Trajectories for Diffusion Image Editing
• EditCrafter: Tuning-free High-Resolution Image Editing via Pretrained Diffusion Model
• Stochastic Optimal Control Sampling for Diffusion Inverse Problems
• UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models

Source: arXiv cs.CV | 2026-09-09