KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image
  Action Editing
Jiancheng Huang Yifan Liu Jin Qin Shifeng Chen
Text-conditioned image editing is a recently emerged and highly practical
task, and its potential is immeasurable. However, most of the concurrent
methods are unable to perform action editing, i.e. they can not produce results
that conform to the action semantics of the editing prompt and preserve the
content of the original image. To solve the problem of action editing, we
propose KV Inversion, a method that can achieve satisfactory reconstruction
performance and action editing, which can solve two major problems: 1) the
edited result can match the corresponding action, and 2) the edited object can
retain the texture and identity of the original real image. In addition, our
method does not require training the Stable Diffusion model itself, nor does it
require scanning a large-scale dataset to perform time-consuming training.