In LBM paper, the noise and the conditioning image are merged into a single composite image.
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In LBM paper, the noise and the conditioning image are merged into a single composite image.
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Unlike other inpainting methods (which typically grey-mask the missing area), LBM replaces the masked region with uniformly sampled random pixels.
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Unlike other inpainting methods (which typically grey-mask the missing area), LBM replaces the masked region with uniformly sampled random pixels.
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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.”
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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.”
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LBM Paper: 
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LBM Paper: 
LBM: Latent Bridge Matching for Fast Image-to-Image Translation (2503.07535)
https://huggingface.co/papers/2503.07535
2503.07535
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https://huggingface.co/papers/2503.07535
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Our fork (work in progress): 
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Our fork (work in progress): 
https://github.com/finegrain-ai/LBM
https://github.com/finegrain-ai/LBM
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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: https://huggingface.co/papers/2503.07535
Our fork (work in progress): https://github.com/finegrain-ai/LBM
🚀
John6666