Elastic Bridge Model for Image Editing

Image editing has two competing goals: it should preserve unedited regions with high fidelity while generating realistic and coherent content in edited regions. Existing data-to-data bridge paradigms struggle to satisfy both. Flow matching often under-edits due to overly deterministic trajectories, whereas Brownian bridges introduce stochasticity via global noise, which harms regions that should remain intact. We interpret this stochasticity-determinism tension through a spatial signal-to-noise ratio (SNR) lens. High SNR yields more deterministic trajectories and better fidelity in unedited regions, while lower SNR increases stochasticity and enables substantial edits. As a result, edited and unedited regions require different SNR levels. To resolve this, we propose Elastic Bridge, which replaces the global noise scale with a spatially adaptive elasticity map to balance SNR across regions. To make this design practical, we introduce Source-Grounded Elasticity, a simple approximation that mitigates the resulting train-inference mismatch. Under fair comparison, Elastic Bridge achieves the best accuracy-preservation trade-off among all compared paradigms and scales effectively to state-of-the-art large-scale models.