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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.20155 |
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| _version_ | 1866916810720280576 |
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| author | Jadhav, Avadhoot Srivastava, Ashutosh Java, Abhinav Singh, Silky Menta, Tarun Ram Jandial, Surgan Krishnamurthy, Balaji |
| author_facet | Jadhav, Avadhoot Srivastava, Ashutosh Java, Abhinav Singh, Silky Menta, Tarun Ram Jandial, Surgan Krishnamurthy, Balaji |
| contents | Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome. The ambiguous nature of certain image edits is better expressed through an exemplar pair, i.e., a pair of images depicting an image before and after an edit respectively. In this work, we tackle exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s), by leveraging pretrained text-to-image diffusion models and multimodal VLMs. Even though our end-to-end pipeline is optimization-free, our experiments demonstrate that it still outperforms baselines on multiple types of edits while being ~4x faster. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Efficient Exemplar Based Image Editing with Multimodal VLMs Jadhav, Avadhoot Srivastava, Ashutosh Java, Abhinav Singh, Silky Menta, Tarun Ram Jandial, Surgan Krishnamurthy, Balaji Computer Vision and Pattern Recognition Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome. The ambiguous nature of certain image edits is better expressed through an exemplar pair, i.e., a pair of images depicting an image before and after an edit respectively. In this work, we tackle exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s), by leveraging pretrained text-to-image diffusion models and multimodal VLMs. Even though our end-to-end pipeline is optimization-free, our experiments demonstrate that it still outperforms baselines on multiple types of edits while being ~4x faster. |
| title | Towards Efficient Exemplar Based Image Editing with Multimodal VLMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.20155 |