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Text-conditional image editing is a very useful task that has recentlyemerged with immeasurable potential. Most current real image editing methodsfirst need to complete the reconstruction of the image, and then editing iscarried out by various methods based on the reconstruction. Most methods useDDIM Inversion for reconstruction, however, DDIM Inversion often fails toguarantee reconstruction performance, i.e., it fails to produce results thatpreserve the original image content. To address the problem of reconstructionfailure, we propose FEC, which consists of three sampling methods, eachdesigned for different editing types and settings. Our three methods of FECachieve two important goals in image editing task: 1) ensuring successfulreconstruction, i.e., sampling to get a generated result that preserves thetexture and features of the original real image. 2) these sampling methods canbe paired with many editing methods and greatly improve the performance ofthese editing methods to accomplish various editing tasks. In addition, none ofour sampling methods require fine-tuning of the diffusion model ortime-consuming training on large-scale datasets. Hence the cost of time as wellas the use of computer memory and computation can be significantly reduced.

FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image Editing