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

2026-09-06

AI Summary

为解决现有图像编辑方法效率低、可塑性差和区域保留不准确的问题，提出基于SDE逆向的MIEdit框架，通过多历史步长预测-校正方案提高编辑质量和稳定性。

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/.

Problem

• diffusion stochastic differential equation
• SDE inversion
• image editing
• regional awareness
• plasticity

Innovation

• SDE Inversion
• Multi-History-Step Scheme
• Semantic Angle Masking