LBM: Latent Bridge Matching for Fast Image-to-Image Translation
This addresses the problem of efficient and versatile image translation for computer vision applications, though it appears incremental as it builds on Bridge Matching in a latent space.
The paper tackles fast image-to-image translation by introducing Latent Bridge Matching (LBM), which achieves state-of-the-art results across various tasks using only a single inference step.
In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. We show that the method can reach state-of-the-art results for various image-to-image tasks using only a single inference step. In addition to its efficiency, we also demonstrate the versatility of the method across different image translation tasks such as object removal, normal and depth estimation, and object relighting. We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation. We provide an implementation at https://github.com/gojasper/LBM.