  Generic image inpainting aims to complete a corrupted image by borrowing
surrounding information, which barely generates novel content. By contrast,
multi-modal inpainting provides more flexible and useful controls on the
inpainted content, \eg, a text prompt can be used to describe an object with
richer attributes, and a mask can be used to constrain the shape of the
inpainted object rather than being only considered as a missing area. We
propose a new diffusion-based model named SmartBrush for completing a missing
region with an object using both text and shape-guidance. While previous work
such as DALLE-2 and Stable Diffusion can do text-guided inapinting they do not
support shape guidance and tend to modify background texture surrounding the
generated object. Our model incorporates both text and shape guidance with
precision control. To preserve the background better, we propose a novel
training and sampling strategy by augmenting the diffusion U-net with
object-mask prediction. Lastly, we introduce a multi-task training strategy by
jointly training inpainting with text-to-image generation to leverage more
training data. We conduct extensive experiments showing that our model
outperforms all baselines in terms of visual quality, mask controllability, and
background preservation.
