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In LBM paper, the noise and the conditioning image are merged into a single composite image.
Unlike other inpainting methods (which typically grey-mask the missing area), LBM replaces the masked region with uniformly sampled random pixels.
Intuitively, since LBM is trained from a text-to-image (T2I) model, those random pixels act as a strong signal to the pretrained model — essentially saying: “This is where you can do your generative magic.”
LBM Paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our fork (work in progress): https://github.com/finegrain-ai/LBM
Unlike other inpainting methods (which typically grey-mask the missing area), LBM replaces the masked region with uniformly sampled random pixels.
Intuitively, since LBM is trained from a text-to-image (T2I) model, those random pixels act as a strong signal to the pretrained model — essentially saying: “This is where you can do your generative magic.”
LBM Paper: LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
Our fork (work in progress): https://github.com/finegrain-ai/LBM