ReasonPix2Pix：面向高级图像编辑的指令推理数据集
ReasonPix2Pix: Instruction Reasoning Dataset for Advanced Image Editing
AI总结：
为解决图像编辑模型缺乏主动推理能力的问题，本文提出ReasonPix2Pix推理指令编辑数据集，通过包含推理指令、真实细粒度图像及增大输入输出差异的数据，使模型在监督微调后于各类指令编辑任务中均表现优异。
摘要：
基于指令的图像编辑致力于使生成模型具备遵循人类书写指令编辑图像的能力。现有方法通常能理解明确具体的指令，但往往缺乏执行主动推理的能力，难以理解隐式或定义不充分的指令。为增强主动推理能力并赋予编辑模型智能，我们提出了ReasonPix2Pix，一个全面的推理注意力指令编辑数据集。该数据集具有以下特征：1) 推理指令，2) 来自细粒度类别的更真实图像，3) 输入与编辑图像之间的差异增大。在监督条件下使用我们的数据集进行微调时，无论任务是否需要推理，模型在指令编辑任务中均展现出卓越的性能。代码将发布于 https://github.com/Jin-Ying/ReasonPix2Pix。
英文摘要：
Instruction-based image editing focuses on equipping a generative model with the capacity to adhere to human-written instructions for editing images. Current approaches typically comprehend explicit and specific instructions. However, they often exhibit a deficiency in executing active reasoning capacities required to comprehend instructions that are implicit or insufficiently defined. To enhance active reasoning capabilities and impart intelligence to the editing model, we introduce ReasonPix2Pix, a comprehensive reasoning-attentive instruction editing dataset. The dataset is characterized by 1) reasoning instruction, 2) more realistic images from fine-grained categories, and 3) increased variances between input and edited images. When fine-tuned with our dataset under supervised conditions, the model demonstrates superior performance in instructional editing tasks, independent of whether the tasks require reasoning or not. The code will be available at https://github.com/Jin-Ying/ReasonPix2Pix.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
