Negative-prompt Inversion: Fast Image Inversion for Editing with
  Text-guided Diffusion Models
Daiki Miyake Akihiro Iohara Yu Saito Toshiyuki Tanaka
In image editing employing diffusion models, it is crucial to preserve the
reconstruction quality of the original image while changing its style. Although
existing methods ensure reconstruction quality through optimization, a drawback
of these is the significant amount of time required for optimization. In this
paper, we propose negative-prompt inversion, a method capable of achieving
equivalent reconstruction solely through forward propagation without
optimization, thereby enabling much faster editing processes. We experimentally
demonstrate that the reconstruction quality of our method is comparable to that
of existing methods, allowing for inversion at a resolution of 512 pixels and
with 50 sampling steps within approximately 5 seconds, which is more than 30
times faster than null-text inversion. Reduction of the computation time by the
proposed method further allows us to use a larger number of sampling steps in
diffusion models to improve the reconstruction quality with a moderate increase
in computation time.