Text-conditional image editing is a very useful task that has recently emerged with immeasurable potential. Most current real image editing methods first need to complete the reconstruction of the image, and then editing is carried out by various methods based on the reconstruction. Most methods use DDIM Inversion for reconstruction, however, DDIM Inversion often fails to guarantee reconstruction performance, i.e., it fails to produce results that preserve the original image content. To address the problem of reconstruction failure, we propose FEC, which consists of three sampling methods, each designed for different editing types and settings. Our three methods of FEC achieve two important goals in image editing task: 1) ensuring successful reconstruction, i.e., sampling to get a generated result that preserves the texture and features of the original real image. 2) these sampling methods can be paired with many editing methods and greatly improve the performance of these editing methods to accomplish various editing tasks. In addition, none of our sampling methods require fine-tuning of the diffusion model or time-consuming training on large-scale datasets. Hence the cost of time as well as the use of computer memory and computation can be significantly reduced.

文本条件图像编辑是一项非常有用且具有巨大潜力的任务。我们提出的FEC通过三种采样方法解决了重构失败的问题，从而实现了成功重构并提升了图像编辑任务的性能。

FEC: 提升真实图像编辑一致性的三种无微调方法