End Token的奇妙案例：使用CLIP的零样本解耦图像编辑
The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP
AI总结：
本文发现Stable Diffusion等扩散模型中常用的CLIP能以零样本方式实现解耦图像编辑，定性和定量实验表明该方法具竞争力，为图像和视频编辑提供了轻量高效的途径。
摘要：
扩散模型在创建高质量图像方面变得突出。然而，不同于以能够以解耦方式编辑图像而闻名的GAN模型，基于扩散的文本到图像模型在不损害图像连贯性的情况下，难以实现同等水平的精确属性操控。在本文中，常用于Stable Diffusion等流行文本到图像扩散模型中的CLIP，能够以零样本方式执行解耦编辑。通过与最先进的编辑方法进行定性和定量比较，我们表明我们的方法产生了有竞争力的结果。这一见解可能为将该方法应用于包括图像和视频编辑在内的各种任务开辟机会，为解耦编辑提供轻量且高效的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
