Title: R-Genie: Reasoning-Guided Generative Image Editing
Primary area: generative models
Keywords: Image Editing, Reasoning Learning, Multimodal Large Language Models, lnstruction Learning

Abstract: While recent advances in image editing have enabled impressive synthesis capabilities, current methods remain constrained by explicit textual instructions and simple editing operations, lacking deep comprehension of implicit user intentions and contextual reasoning. In this work, we introduce a novel reasoning-guided generative image editing paradigm, which generates images based on complex, multi-faceted instructions accepting world knowledge and intention inference. To facilitate this paradigm, we first construct a comprehensive dataset featuring over 1,000 image-instruction-edit triples that incorporate rich reasoning contexts and world knowledge. We then propose R-Genie: a reasoning-guided generative image editor, which synergizes the generation power of diffusion models with advanced reasoning capabilities of multi-modal large language models. R-Genie leverages a reasoning-attention mechanism to bridge linguistic understanding with visual synthesis, enabling it to handle intricate editing requests involving abstract user intentions and contextual reasoning relations. Extensive experimental results validate that R-Genie can equip diffusion models with advanced reasoning-based editing capabilities, unlocking new potentials for intelligent image synthesis.

TL;DR: We introduce a new image editing paradigm: reasoning-guided generative editing, which synthesizes images based on complex, multi-faceted textual queries accepting world knowledge and intention inference.