  Advanced image editing techniques, particularly inpainting, are essential for
seamlessly removing unwanted elements while preserving visual integrity.
Traditional GAN-based methods have achieved notable success, but recent
advancements in diffusion models have produced superior results due to their
training on large-scale datasets, enabling the generation of remarkably
realistic inpainted images. Despite their strengths, diffusion models often
struggle with object removal tasks without explicit guidance, leading to
unintended hallucinations of the removed object. To address this issue, we
introduce CLIPAway, a novel approach leveraging CLIP embeddings to focus on
background regions while excluding foreground elements. CLIPAway enhances
inpainting accuracy and quality by identifying embeddings that prioritize the
background, thus achieving seamless object removal. Unlike other methods that
rely on specialized training datasets or costly manual annotations, CLIPAway
provides a flexible, plug-and-play solution compatible with various
diffusion-based inpainting techniques.
