  Image inpainting aims to fill in the missing pixels with visually coherent
and semantically plausible content. Despite the great progress brought from
deep generative models, this task still suffers from i. the difficulties in
large-scale realistic data collection and costly model training; and ii. the
intrinsic limitations in the traditionally user-defined binary masks on objects
with unclear boundaries or transparent texture. In this paper, we propose
MagicRemover, a tuning-free method that leverages the powerful diffusion models
for text-guided image inpainting. We introduce an attention guidance strategy
to constrain the sampling process of diffusion models, enabling the erasing of
instructed areas and the restoration of occluded content. We further propose a
classifier optimization algorithm to facilitate the denoising stability within
less sampling steps. Extensive comparisons are conducted among our MagicRemover
and state-of-the-art methods including quantitative evaluation and user study,
demonstrating the significant improvement of MagicRemover on high-quality image
inpainting. We will release our code at https://github.com/exisas/Magicremover.
