The tremendous progress in neural image generation, coupled with the
emergence of seemingly omnipotent vision-language models has finally enabled
text-based interfaces for creating and editing images. Handling generic images
requires a diverse underlying generative model, hence the latest works utilize
diffusion models, which were shown to surpass GANs in terms of diversity. One
major drawback of diffusion models, however, is their relatively slow inference
time. In this paper, we present an accelerated solution to the task of local
text-driven editing of generic images, where the desired edits are confined to
a user-provided mask. Our solution leverages a recent text-to-image Latent
Diffusion Model (LDM), which speeds up diffusion by operating in a
lower-dimensional latent space. We first convert the LDM into a local image
editor by incorporating Blended Diffusion into it. Next we propose an
optimization-based solution for the inherent inability of this LDM to
accurately reconstruct images. Finally, we address the scenario of performing
local edits using thin masks. We evaluate our method against the available
baselines both qualitatively and quantitatively and demonstrate that in
addition to being faster, our method achieves better precision than the
baselines while mitigating some of their artifacts. Project page is available
at this https URL

本研究介绍了一种基于文本的图像编辑方法，利用最新的扩散模型对一些基于文本主题的地方图像进行编辑，通过结合扩散模型的速度和 Blended Diffusion，提高了编辑的效率，并通过优化方法来解决扩散模型无法完美重建图像的问题，实现了比当前方法更高的精度和速度