Image inpainting is the process of taking an image and generating lost or
intentionally occluded portions. Inpainting has countless applications
including restoring previously damaged pictures, restoring the quality of
images that have been degraded due to compression, and removing unwanted
objects/text. Modern inpainting techniques have shown remarkable ability in
generating sensible completions for images with mask occlusions. In our paper,
an overview of the progress of inpainting techniques will be provided, along
with identifying current leading approaches, focusing on their strengths and
weaknesses. A critical gap in these existing models will be addressed, focusing
on the ability to prompt and control what exactly is generated. We will
additionally justify why we think this is the natural next progressive step
that inpainting models must take, and provide multiple approaches to
implementing this functionality. Finally, we will evaluate the results of our
approaches by qualitatively checking whether they generate high-quality images
that correctly inpaint regions with the objects that they are instructed to
produce.

图像修复是将图像中的缺失或故意遮挡部分恢复出来的过程，在现代修复技术中，我们提供了当前主要方法的概述和评估，并着重解决了现有模型在生成过程中缺乏提示和控制能力的问题，并提供了多种实现该功能的方法。最终，通过对生成的高质量图像进行定性检查，我们评估了我们的方法的结果是否能正确地修复指定区域。