  Employing the latent space of pretrained generators has recently been shown
to be an effective means for GAN-based face manipulation. The success of this
approach heavily relies on the innate disentanglement of the latent space axes
of the generator. However, face manipulation often intends to affect local
regions only, while common generators do not tend to have the necessary spatial
disentanglement. In this paper, we build on the StyleGAN generator, and present
a method that explicitly encourages face manipulation to focus on the intended
regions by incorporating learned attention maps. During the generation of the
edited image, the attention map serves as a mask that guides a blending between
the original features and the modified ones. The guidance for the latent space
edits is achieved by employing CLIP, which has recently been shown to be
effective for text-driven edits. We perform extensive experiments and show that
our method can perform disentangled and controllable face manipulations based
on text descriptions by attending to the relevant regions only. Both
qualitative and quantitative experimental results demonstrate the superiority
of our method for facial region editing over alternative methods.
