FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image
  Editing
Songyan Chen Jiancheng Huang
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.