  Recent advances in diffusion models enable many powerful instruments for
image editing. One of these instruments is text-driven image manipulations:
editing semantic attributes of an image according to the provided text
description. % Popular text-conditional diffusion models offer various
high-quality image manipulation methods for a broad range of text prompts.
Existing diffusion-based methods already achieve high-quality image
manipulations for a broad range of text prompts. However, in practice, these
methods require high computation costs even with a high-end GPU. This greatly
limits potential real-world applications of diffusion-based image editing,
especially when running on user devices.
  In this paper, we address efficiency of the recent text-driven editing
methods based on unconditional diffusion models and develop a novel algorithm
that learns image manipulations 4.5-10 times faster and applies them 8 times
faster. We carefully evaluate the visual quality and expressiveness of our
approach on multiple datasets using human annotators. Our experiments
demonstrate that our algorithm achieves the quality of much more expensive
methods. Finally, we show that our approach can adapt the pretrained model to
the user-specified image and text description on the fly just for 4 seconds. In
this setting, we notice that more compact unconditional diffusion models can be
considered as a rational alternative to the popular text-conditional
counterparts.
