Source Prompt Disentangled Inversion for Boosting Image Editability with
  Diffusion Models
Ruibin Li Ruihuang Li Song Guo Lei Zhang
Text-driven diffusion models have significantly advanced the image editing
performance by using text prompts as inputs. One crucial step in text-driven
image editing is to invert the original image into a latent noise code
conditioned on the source prompt. While previous methods have achieved
promising results by refactoring the image synthesizing process, the inverted
latent noise code is tightly coupled with the source prompt, limiting the image
editability by target text prompts. To address this issue, we propose a novel
method called Source Prompt Disentangled Inversion (SPDInv), which aims at
reducing the impact of source prompt, thereby enhancing the text-driven image
editing performance by employing diffusion models. To make the inverted noise
code be independent of the given source prompt as much as possible, we indicate
that the iterative inversion process should satisfy a fixed-point constraint.
Consequently, we transform the inversion problem into a searching problem to
find the fixed-point solution, and utilize the pre-trained diffusion models to
facilitate the searching process. The experimental results show that our
proposed SPDInv method can effectively mitigate the conflicts between the
target editing prompt and the source prompt, leading to a significant decrease
in editing artifacts. In addition to text-driven image editing, with SPDInv we
can easily adapt customized image generation models to localized editing tasks
and produce promising performance. The source code are available at
https://github.com/leeruibin/SPDInv.