  Recently, text-to-image denoising diffusion probabilistic models (DDPMs) have
demonstrated impressive image generation capabilities and have also been
successfully applied to image inpainting. However, in practice, users often
require more control over the inpainting process beyond textual guidance,
especially when they want to composite objects with customized appearance,
color, shape, and layout. Unfortunately, existing diffusion-based inpainting
methods are limited to single-modal guidance and require task-specific
training, hindering their cross-modal scalability. To address these
limitations, we propose Uni-paint, a unified framework for multimodal
inpainting that offers various modes of guidance, including unconditional,
text-driven, stroke-driven, exemplar-driven inpainting, as well as a
combination of these modes. Furthermore, our Uni-paint is based on pretrained
Stable Diffusion and does not require task-specific training on specific
datasets, enabling few-shot generalizability to customized images. We have
conducted extensive qualitative and quantitative evaluations that show our
approach achieves comparable results to existing single-modal methods while
offering multimodal inpainting capabilities not available in other methods.
Code will be available at https://github.com/ysy31415/unipaint.
