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. Our code
available at this https URL

我们在这篇论文中提出了一种名为 InstDiffEdit 的新型高效图像编辑方法，它利用现有扩散模型的跨模态注意力能力，在扩散步骤中实现了即时的遮罩引导。我们将 InstDiffEdit 与 SOTA 方法进行了广泛的实验证明，结果显示它在图像质量和编辑结果方面不仅优于 SOTA 方法，而且推理速度快 5 至 6 倍。