SpecRef: A Fast Training-free Baseline of Specific Reference-Condition
  Real Image Editing
Songyan Chen Jiancheng Huang
Text-conditional image editing based on large diffusion generative model has
attracted the attention of both the industry and the research community. Most
existing methods are non-reference editing, with the user only able to provide
a source image and text prompt. However, it restricts user's control over the
characteristics of editing outcome. To increase user freedom, we propose a new
task called Specific Reference Condition Real Image Editing, which allows user
to provide a reference image to further control the outcome, such as replacing
an object with a particular one. To accomplish this, we propose a fast baseline
method named SpecRef. Specifically, we design a Specific Reference Attention
Controller to incorporate features from the reference image, and adopt a mask
mechanism to prevent interference between editing and non-editing regions. We
evaluate SpecRef on typical editing tasks and show that it can achieve
satisfactory performance. The source code is available on
https://github.com/jingjiqinggong/specp2p.