SoEdit: Improving Instruction-Driven Object Editing by Focusing on a Single Object Within a Cropped Region

Recently, instruction-driven image editing methods have demonstrated promising capabilities, requiring only a brief text to guide image modifications. However, most of them often yield suboptimal results for object editing in complex scenes, due to two major defects: (1) Over-editing, where unintended regions of the image are inadvertently altered; (2) Inability to precisely adhere to instructions, particularly in scenes with numerous elements. To resolve these issues, we propose a Single object Editing scheme, termed SoEdit, which distills complex editing tasks into single-object editing within cropped regions through a pipeline that integrates task parsing, object localization, editing, and context blending. This approach minimizes interference from irrelevant areas, ensures proper object size and placement, and ultimately enhances model performance. Furthermore, we introduce a lightweightSpatially-Adaptive Mixture of Experts (SAMOE)to better model spatial heterogeneity, enabling tokenwise adaptive processing and further enhancing the overall editing capability with minimal additional parameters. Moreover, we introduce a large-scale object-centric dataset to further optimize the model. Extensive experiments demonstrate that SoEdit outperforms existing methods, especially in precise responses to fine-grained editing requirements, such as multi-action and quantity-sensitive object editing.