The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP
<|reference_start|>The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP: 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.<|reference_end|>
@article{yesiltepe2024the,
    title={The Curious Case of End Token: A Zero-Shot Disentangled Image Editing
  using CLIP},
    author={Hidir Yesiltepe, Yusuf Dalva, Pinar Yanardag},
    journal={arXiv preprint arXiv:2406.00457},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00457},
    primaryClass={cs.CV}
}