In recent years, instruction-based image editing methods have garnered significant attention in image editing. However, despite encompassing a wide range of editing priors, these methods are helpless when handling editing tasks that are challenging to accurately describe through language. We propose InstructBrush, an inversion method for instruction-based image editing methods to bridge this gap. It extracts editing effects from exemplar image pairs as editing instructions, which are further applied for image editing. Two key techniques are introduced into InstructBrush, Attention-based Instruction Optimization and Transformation-oriented Instruction Initialization, to address the limitations of the previous method in terms of inversion effects and instruction generalization. To explore the ability of instruction inversion methods to guide image editing in open scenarios, we establish a TransformationOriented Paired Benchmark (TOP-Bench), which contains a rich set of scenes and editing types. The creation of this benchmark paves the way for further exploration of instruction inversion. Quantitatively and qualitatively, our approach achieves superior performance in editing and is more semantically consistent with the target editing effects.

中文翻译：

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InstructBrush：学习基于注意力的图像编辑指令优化

近年来，基于指令的图像编辑方法在图像编辑领域引起了广泛关注。然而，尽管涵盖了广泛的编辑先验，但这些方法在处理难以通过语言准确描述的编辑任务时却无能为力。我们提出了 InstructBrush，一种基于指令的图像编辑方法的反转方法来弥补这一差距。它从示例图像对中提取编辑效果作为编辑指令，进一步应用于图像编辑。 InstructBrush引入了两项关键技术，即基于注意力的指令优化和面向变换的指令初始化，以解决先前方法在反转效果和指令泛化方面的局限性。为了探索指令反转方法在开放场景中指导图像编辑的能力，我们建立了一个面向变换的配对基准（TOP-Bench），其中包含丰富的场景和编辑类型。该基准的创建为进一步探索指令反转铺平了道路。在定量和定性上，我们的方法在编辑方面取得了优异的性能，并且在语义上与目标编辑效果更加一致。