Conditioning Matters: Stabilizing Inversion and Attention in Diffusion Image Editing
Inversion-based image editing offers flexible and training-free control but still struggles with inversion accuracy and the trade-off between editing fidelity and background preservation. While recent methods improve inversion formulations or attention interactions, the role of textual conditioning in shaping diffusion dynamics and editing behavior remains underexplored. We show both empirically and theoretically that the precision of textual conditioning influences inversion stability by modulating the geometry of the diffusion velocity field, while also affecting the consistency of cross-branch attention during editing. These effects directly impact background preservation and semantic ...
| Authors | Zheyuan Zhan, Hongchen Li, Can Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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