InferEdit: An instruction-based system with a multimodal LLM for complex multi-target image editing
Year: 2025
Type: article
Abstract: To address the limitations of existing instruction-based image editing methods in handling complex Multi-target instructions and maintaining semantic consistency, we present InferEdit, a training-free image editing system driven by a Multimodal Large Language Model (MLLM). The system parses complex multi-target instructions into sequential subtasks and performs editing iteratively through target localization and semantic reasoning. Furthermore, to adaptively select the most suitable editing models, we construct the evaluation dataset InferDataset to evaluate various editing models on three types of tasks: object removal, object replacement, and local editing. Based on a comprehensive scoring mechanism, we build Binary Search Trees (BSTs) for different editing types to facilitate model scheduling. Experiments demonstrate that InferEdit outperforms existing methods in handling complex instructions while maintaining semantic consistency and visual quality.
Source: Visual Informatics
Authors: Zhiyong Huang, Yali She, MengLi Xiang, TuoJun Ding
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7077509948