R-Genie：推理引导的生成式图像编辑
R-Genie: Reasoning-Guided Generative Image Editing
AI总结：
本文提出推理引导的生成式图像编辑范式及模型R-Genie，通过构建含1000余条推理编辑数据的数据集，并结合推理-注意力机制融合扩散模型与多模态大语言模型，以处理复杂意图和上下文推理编辑任务。
摘要：
尽管图像编辑的最新进展已展现出令人瞩目的视觉合成能力，但现有方法仍受限于显式文本指令和有限的编辑操作，缺乏对隐式用户意图和上下文推理的深层理解。在本工作中，我们引入一种新的图像编辑范式：推理引导的生成式编辑，该范式基于复杂、多方面的文本查询合成图像，并接纳世界知识与意图推断。为促进该任务，我们首先构建了一个包含超过1000个图像-指令-编辑三元组的综合数据集，其中融入了丰富的推理上下文和现实世界知识。随后，我们提出R-Genie：一个推理引导的生成式图像编辑器，它将扩散模型的生成能力与多模态大语言模型的先进推理能力协同结合。R-Genie引入了一种推理-注意力机制，以桥接语言理解与视觉合成，使其能够处理涉及抽象用户意图和上下文推理关系的复杂编辑请求。大量实验结果验证了R-Genie能够赋予扩散模型基于推理的高级编辑能力，从而释放智能图像合成的新潜力。
英文摘要：
While recent advances in image editing have enabled impressive visual synthesis capabilities, current methods remain constrained by explicit textual instructions and limited editing operations, lacking deep comprehension of implicit user intentions and contextual reasoning. In this work, 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. To facilitate this task, we first construct a comprehensive dataset featuring over 1,000 image-instruction-edit triples that incorporate rich reasoning contexts and real-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 multimodal large language models. R-Genie incorporates 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.
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