Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.12585 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909141993259008 |
|---|---|
| author | Alharbi, Yazeed Wonka, Peter |
| author_facet | Alharbi, Yazeed Wonka, Peter |
| contents | We present a novel, training-free approach for textual editing of real images using diffusion models. Unlike prior methods that rely on computationally expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA) to efficiently preserve image details. We demonstrate how the diffusion process is amenable to spatial guidance using a reference image, leading to semantically coherent edits. This eliminates the need for complex optimization and costly model finetuning, resulting in significantly faster editing compared to previous methods. Additionally, our method avoids the storage requirements associated with large finetuned models. These advantages make our approach particularly well-suited for editing on mobile devices and applications demanding rapid response times. While simple and fast, our method achieves 62-71\% preference in a user-study and significantly better model-based editing strength and image preservation scores. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12585 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LASPA: Latent Spatial Alignment for Fast Training-free Single Image Editing Alharbi, Yazeed Wonka, Peter Computer Vision and Pattern Recognition We present a novel, training-free approach for textual editing of real images using diffusion models. Unlike prior methods that rely on computationally expensive finetuning, our approach leverages LAtent SPatial Alignment (LASPA) to efficiently preserve image details. We demonstrate how the diffusion process is amenable to spatial guidance using a reference image, leading to semantically coherent edits. This eliminates the need for complex optimization and costly model finetuning, resulting in significantly faster editing compared to previous methods. Additionally, our method avoids the storage requirements associated with large finetuned models. These advantages make our approach particularly well-suited for editing on mobile devices and applications demanding rapid response times. While simple and fast, our method achieves 62-71\% preference in a user-study and significantly better model-based editing strength and image preservation scores. |
| title | LASPA: Latent Spatial Alignment for Fast Training-free Single Image Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.12585 |