  Deep generative models, like GANs, have considerably improved the state of
the art in image synthesis, and are able to generate near photo-realistic
images in structured domains such as human faces. Based on this success, recent
work on image editing proceeds by projecting images to the GAN latent space and
manipulating the latent vector. However, these approaches are limited in that
only images from a narrow domain can be transformed, and with only a limited
number of editing operations. We propose FlexIT, a novel method which can take
any input image and a user-defined text instruction for editing. Our method
achieves flexible and natural editing, pushing the limits of semantic image
translation. First, FlexIT combines the input image and text into a single
target point in the CLIP multimodal embedding space. Via the latent space of an
auto-encoder, we iteratively transform the input image toward the target point,
ensuring coherence and quality with a variety of novel regularization terms. We
propose an evaluation protocol for semantic image translation, and thoroughly
evaluate our method on ImageNet. Code will be made publicly available.
