The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP
This provides a lightweight and efficient approach for disentangled image and video editing, though it appears incremental as it builds on existing CLIP and diffusion model frameworks.
The paper tackles the problem of precise attribute manipulation in diffusion-based text-to-image models without compromising coherence, showing that CLIP enables zero-shot disentangled editing with competitive results compared to state-of-the-art methods.
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.