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arXiv · 2406.00457

The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP

Hidir Yesiltepe·Yusuf Dalva·Pinar Yanardag
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Abstract

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.

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Hidir Yesiltepe, Yusuf Dalva, Pinar Yanardag. 2024-06-01. The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP. https://arxiv.org/abs/2406.00457

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