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.

翻译：基于文本的扩散模型通过使用文本提示作为输入，显著提升了图像编辑性能。文本驱动图像编辑的关键步骤之一是将原始图像反演为以源提示为条件的潜在噪声编码。尽管先前方法通过重构图像合成过程已取得令人满意的结果，但反演得到的潜在噪声编码与源提示紧密耦合，限制了目标文本提示对图像的编辑能力。为解决这一问题，我们提出了一种名为源提示解耦反演（SPDInv）的新方法，旨在降低源提示的影响，从而通过扩散模型增强文本驱动的图像编辑性能。为使反演的噪声编码尽可能独立于给定的源提示，我们指出迭代反演过程应满足不动点约束。因此，我们将反演问题转化为寻找不动点解的搜索问题，并利用预训练的扩散模型促进搜索过程。实验结果表明，我们提出的SPDInv方法能有效缓解目标编辑提示与源提示之间的冲突，显著减少编辑伪影。除文本驱动图像编辑外，通过SPDInv我们可以轻松将定制化图像生成模型适配于局部编辑任务，并取得优异性能。源代码发布于https://github.com/leeruibin/SPDInv。