Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness
Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing. Existing fixed-point inversion methods improve inversion accuracy by formulating each inversion step as a fixed-point problem, but they lack a principled mechanism for selecting among multiple fixed-point solutions that can arise in practice. We observe that different selections induce different inversion trajectories, leading to substantial variation in reconstruction and editing quality. For rectified flows, we further find that this variation is closely associated with trajectory straightness, motivating straightness as a ...
| Authors | Semin Kim, Jihwan Yoon, Seunghoon Hong |
|---|---|
| Primary category | cs.CV |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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