  In this paper, we focus on resolving the problem of image outpainting, which
aims to extrapolate the surrounding parts given the center contents of an
image. Although recent works have achieved promising performance, the lack of
versatility and customization hinders their practical applications in broader
scenarios. Therefore, this work presents a novel image outpainting framework
that is capable of customizing the results according to the requirement of
users. First of all, we take advantage of a Multimodal Large Language Model
(MLLM) that automatically extracts and organizes the corresponding textual
descriptions of the masked and unmasked part of a given image. Accordingly, the
obtained text prompts are introduced to endow our model with the capacity to
customize the outpainting results. In addition, a special Cross-Attention
module, namely Center-Total-Surrounding (CTS), is elaborately designed to
enhance further the the interaction between specific space regions of the image
and corresponding parts of the text prompts. Note that unlike most existing
methods, our approach is very resource-efficient since it is just slightly
fine-tuned on the off-the-shelf stable diffusion (SD) model rather than being
trained from scratch. Finally, the experimental results on three commonly used
datasets, i.e. Scenery, Building, and WikiArt, demonstrate our model
significantly surpasses the SoTA methods. Moreover, versatile outpainting
results are listed to show its customized ability.
