  Diffusion-based Image Editing (DIE) is an emerging research hot-spot, which
often applies a semantic mask to control the target area for diffusion-based
editing. However, most existing solutions obtain these masks via manual
operations or off-line processing, greatly reducing their efficiency. In this
paper, we propose a novel and efficient image editing method for Text-to-Image
(T2I) diffusion models, termed Instant Diffusion Editing(InstDiffEdit). In
particular, InstDiffEdit aims to employ the cross-modal attention ability of
existing diffusion models to achieve instant mask guidance during the diffusion
steps. To reduce the noise of attention maps and realize the full automatics,
we equip InstDiffEdit with a training-free refinement scheme to adaptively
aggregate the attention distributions for the automatic yet accurate mask
generation. Meanwhile, to supplement the existing evaluations of DIE, we
propose a new benchmark called Editing-Mask to examine the mask accuracy and
local editing ability of existing methods. To validate InstDiffEdit, we also
conduct extensive experiments on ImageNet and Imagen, and compare it with a
bunch of the SOTA methods. The experimental results show that InstDiffEdit not
only outperforms the SOTA methods in both image quality and editing results,
but also has a much faster inference speed, i.e., +5 to +6 times.
