  Automatic image editing has great demands because of its numerous
applications, and the use of natural language instructions is essential to
achieving flexible and intuitive editing as the user imagines. A pioneering
work in text-driven image editing, StyleCLIP, finds an edit direction in the
CLIP space and then edits the image by mapping the direction to the StyleGAN
space. At the same time, it is difficult to tune appropriate inputs other than
the original image and text instructions for image editing. In this study, we
propose a method to construct the edit direction adaptively in the StyleGAN and
CLIP spaces with SVM. Our model represents the edit direction as a normal
vector in the CLIP space obtained by training a SVM to classify positive and
negative images. The images are retrieved from a large-scale image corpus,
originally used for pre-training StyleGAN, according to the CLIP similarity
between the images and the text instruction. We confirmed that our model
performed as well as the StyleCLIP baseline, whereas it allows simple inputs
without increasing the computational time.
