Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions
This addresses the challenge of realistic and scalable image editing for AI applications, offering a novel paradigm that avoids dataset construction bottlenecks.
The paper tackles the problem of text-driven image editing by developing a method that uses widely available text-image pairs instead of requiring curated editing datasets, achieving state-of-the-art performance with high-fidelity and precise results across various benchmarks.
Current text-driven image editing methods typically follow one of two directions: relying on large-scale, high-quality editing pair datasets to improve editing precision and diversity, or exploring alternative dataset-free techniques. However, constructing large-scale editing datasets requires carefully designed pipelines, is time-consuming, and often results in unrealistic samples or unwanted artifacts. Meanwhile, dataset-free methods may suffer from limited instruction comprehension and restricted editing capabilities. Faced with these challenges, the present work develops a novel paradigm for instruction-driven image editing that leverages widely available and enormous text-image pairs, instead of relying on editing pair datasets. Our approach introduces a multi-scale learnable region to localize and guide the editing process. By treating the alignment between images and their textual descriptions as supervision and learning to generate task-specific editing regions, our method achieves high-fidelity, precise, and instruction-consistent image editing. Extensive experiments demonstrate that the proposed approach attains state-of-the-art performance across various tasks and benchmarks, while exhibiting strong adaptability to various types of generative models.