  Diffusion models have become prominent in creating high-quality images.
However, unlike GAN models celebrated for their ability to edit images in a
disentangled manner, diffusion-based text-to-image models struggle to achieve
the same level of precise attribute manipulation without compromising image
coherence. In this paper, CLIP which is often used in popular text-to-image
diffusion models such as Stable Diffusion is capable of performing disentangled
editing in a zero-shot manner. Through both qualitative and quantitative
comparisons with state-of-the-art editing methods, we show that our approach
yields competitive results. This insight may open opportunities for applying
this method to various tasks, including image and video editing, providing a
lightweight and efficient approach for disentangled editing.
