MSHEdit: Enhanced Text-Driven Image Editing via Advanced Diffusion Model Architecture

To address limitations in structural preservation and detail fidelity in existing text-driven image editing methods, we propose MSHEdit—a novel editing framework built upon a pre-trained diffusion model. MSHEdit is designed to achieve high semantic alignment during image editing without the need for additional training or fine-tuning. The framework integrates two key components: the High-Order Stable Diffusion Sampler (HOS-DEIS) and the Multi-Scale Window Residual Bridge Attention Module (MS-WRBA). HOS-DEIS enhances sampling precision and detail recovery by employing high-order integration and dynamic error compensation, while MS-WRBA improves editing region localization and edge blending through multi-scale window partitioning and dual-path normalization. Extensive experiments on public datasets including DreamBench-v2 and DreamBench++ demonstrate that compared to recent mainstream models, MSHEdit reduces structural distance by 2% and background LPIPS by 1.2%. These results demonstrate its ability to achieve natural transitions between edited regions and backgrounds in complex scenes while effectively mitigating object edge blurring. MSHEdit exhibits excellent structural preservation, semantic consistency, and detail restoration, providing an efficient and generalizable solution for high-quality text-driven image editing.