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Automatic image editing has great demands because of its numerousapplications, and the use of natural language instructions is essential toachieving flexible and intuitive editing as the user imagines. A pioneeringwork in text-driven image editing, StyleCLIP, finds an edit direction in theCLIP space and then edits the image by mapping the direction to the StyleGANspace. At the same time, it is difficult to tune appropriate inputs other thanthe original image and text instructions for image editing. In this study, wepropose a method to construct the edit direction adaptively in the StyleGAN andCLIP spaces with SVM. Our model represents the edit direction as a normalvector in the CLIP space obtained by training a SVM to classify positive andnegative images. The images are retrieved from a large-scale image corpus,originally used for pre-training StyleGAN, according to the CLIP similaritybetween the images and the text instruction. We confirmed that our modelperformed as well as the StyleCLIP baseline, whereas it allows simple inputswithout increasing the computational time.

Robust Text-driven Image Editing Method that Adaptively Explores Directions in Latent Spaces of StyleGAN and CLIP