The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909214676353024 |
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| author | Yesiltepe, Hidir Dalva, Yusuf Yanardag, Pinar |
| author_facet | Yesiltepe, Hidir Dalva, Yusuf Yanardag, Pinar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP Yesiltepe, Hidir Dalva, Yusuf Yanardag, Pinar Computer Vision and Pattern Recognition 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. |
| title | The Curious Case of End Token: A Zero-Shot Disentangled Image Editing using CLIP |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.00457 |