On the Controllability-Fidelity Frontier in Diffusion Editing
Diffusion-based generative models enable powerful image editing capabilities, but achieving precise control while maintaining fidelity and safety remains challenging. We present a comprehensive theoretical and empirical study of controllable diffusion-based image editing, analyzing the trade-offs between adherence to user intent, preservation of non-target content, and output quality. Our work spans text- and mask-guided edits, point/drag manipulation, and inversion-based pipelines. We derive mathematical formulations of editing objectives and analyze dynamics of noise injection, score guidance, and inversion error. We provide theoretical bounds on reconstruction error, stability under re...
| Authors | Yi Hu, Leying Yi, Emily Davis et al. |
|---|---|
| Primary category | cs.GR |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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