With the advantages of fast inference and human-friendly flexible
manipulation, image-agnostic style manipulation via text guidance enables new
applications that were not previously available. The state-of-the-art
text-guided image-agnostic manipulation method embeds the representation of
each channel of StyleGAN independently in the Contrastive Language-Image
Pre-training (CLIP) space, and provides it in the form of a Dictionary to
quickly find out the channel-wise manipulation direction during inference time.
However, in this paper we argue that this dictionary which is constructed by
controlling single channel individually is limited to accommodate the
versatility of text guidance since the collective and interactive relation
among multiple channels are not considered. Indeed, we show that it fails to
discover a large portion of manipulation directions that can be found by
existing methods, which manually manipulates latent space without texts. To
alleviate this issue, we propose a novel method that learns a Dictionary, whose
entry corresponds to the representation of a single channel, by taking into
account the manipulation effect coming from the interaction with multiple other
channels. We demonstrate that our strategy resolves the inability of previous
methods in finding diverse known directions from unsupervised methods and
unknown directions from random text while maintaining the real-time inference
speed and disentanglement ability.

提出了一种新的方法用于基于文本指导的图像风格操作，构建了一个字典来实现效果，已有方法存在很多的局限性，自己提出的方法可以同时处理多个通道间的互动关系，提高能发现未知方向的能力，同时保持了实时性和分离度。