  Existing solutions to image editing tasks suffer from several issues. Though
achieving remarkably satisfying generated results, some supervised methods
require huge amounts of paired training data, which greatly limits their
usages. The other unsupervised methods take full advantage of large-scale
pre-trained priors, thus being strictly restricted to the domains where the
priors are trained on and behaving badly in out-of-distribution cases. The task
we focus on is how to enable the users to customize their desired effects
through only few image pairs. In our proposed framework, a novel few-shot
learning mechanism based on the directional transformations among samples is
introduced and expands the learnable space exponentially. Adopting a diffusion
model pipeline, we redesign the condition calculating modules in our model and
apply several technical improvements. Experimental results demonstrate the
capabilities of our method in various cases.
