  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.
