SeedEdit: Align Image Re-Generation to Image Editing
About
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.
Yichun Shi, Peng Wang, Weilin Huang• 2024
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Image Editing | GEdit-Bench-EN v1.0 (Full set) | G Score (SC)7.222 | 22 | |
| Object Replacement and Style Blending | Object Replacement and Style Blending (800 pairs) (test) | BOSM0.465 | 11 | |
| Object Replacement and Object Blending | Unsplash 4,000 samples (test) | BOM0.5486 | 10 | |
| Image Editing | GEdit-Bench-CN 1.0 (Full set) | G_SC (Generated Content Score)7.168 | 10 | |
| Image Editing | GEdit-Bench-EN Intersection subset v1.0 | G_SC7.396 | 9 | |
| Image Editing | GEdit-Bench-CN Intersection 1.0 | G_SC7.228 | 5 | |
| Image Editing | GEdit-Bench Intersection subset (IS) | UP6.32 | 4 | |
| Image Editing | GEdit-Bench Full set | UP5.678 | 4 |
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