SeedEdit: Align Image Re-Generation to Image Editing

Fuente: arXiv
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Hauptverfasser: Shi, Yichun, Wang, Peng, Huang, Weilin
Format: Preprint
Veröffentlicht: 2024
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author Shi, Yichun
Wang, Peng
Huang, Weilin
author_facet Shi, Yichun
Wang, Peng
Huang, Weilin
contents We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. image reconstruction, and generating a new image, i.e. image re-generation. To this end, we start from a weak generator (text-to-image model) that creates diverse pairs between such two directions and gradually align it into a strong image editor that well balances between the two tasks. SeedEdit can achieve more diverse and stable editing capability over prior image editing methods, enabling sequential revision over images generated by diffusion models.
format Preprint
id arxiv_https___arxiv_org_abs_2411_06686
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SeedEdit: Align Image Re-Generation to Image Editing
Shi, Yichun
Wang, Peng
Huang, Weilin
Computer Vision and Pattern Recognition
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. image reconstruction, and generating a new image, i.e. image re-generation. To this end, we start from a weak generator (text-to-image model) that creates diverse pairs between such two directions and gradually align it into a strong image editor that well balances between the two tasks. SeedEdit can achieve more diverse and stable editing capability over prior image editing methods, enabling sequential revision over images generated by diffusion models.
title SeedEdit: Align Image Re-Generation to Image Editing
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2411.06686