Inverse Bridge Matching Distillation
Learning diffusion bridge models is easy; making them fast and practical is
an art. Diffusion bridge models (DBMs) are a promising extension of diffusion
models for applications in image-to-image translation. However, like many
modern diffusion and flow models, DBMs suffer from the problem of slow
inference. To address it, we propose a novel distillation technique based on
the inverse bridge matching formulation and derive the tractable objective to
solve it in practice. Unlike previously developed DBM distillation techniques,
the proposed method can distill both conditional and unconditional types of
DBMs, distill models in a one-step generator, and use only the corrupted images
for training. We evaluate our approach for both conditional and unconditional
types of bridge matching on a wide set of setups, including super-resolution,
JPEG restoration, sketch-to-image, and other tasks, and show that our
distillation technique allows us to accelerate the inference of DBMs from 4x to
100x and even provide better generation quality than used teacher model
depending on particular setup.
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Nikita Gushchin
claimed_verified
2025-02-05T10:14:26.177Z
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Nikita Gushchin
user
ngushchin
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David Li
claimed_verified
2025-02-05T10:14:24.236Z
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David Li
user
kekchpek
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Daniil Selikhanovych
admin_assigned
2025-02-05T10:17:09.668Z
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Daniil Selikhanovych
user
apryc1
67a2ad6ac7caec9bf5a45e64
Evgeny Burnaev
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Dmitry Baranchuk
admin_assigned
2025-02-05T10:17:26.518Z
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Dmitry Baranchuk
user
dbaranchuk
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Alexander Korotin
admin_assigned
2025-02-05T10:17:33.816Z
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/avatars/ca5fd356e3656e1beacb5a28ecaad5be.svg
Alexander Korotin
user
akorotin
Inverse Bridge Matching Distillation
Learning diffusion bridge models is easy; making them fast and practical is
an art. Diffusion bridge models (DBMs) are a promising extension of diffusion
models for applications in image-to-image translation. However, like many
modern diffusion and flow models, DBMs suffer from the problem of slow
inference. To address it, we propose a novel distillation technique based on
the inverse bridge matching formulation and derive the tractable objective to
solve it in practice. Unlike previously developed DBM distillation techniques,
the proposed method can distill both conditional and unconditional types of
DBMs, distill models in a one-step generator, and use only the corrupted images
for training. We evaluate our approach for both conditional and unconditional
types of bridge matching on a wide set of setups, including super-resolution,
JPEG restoration, sketch-to-image, and other tasks, and show that our
distillation technique allows us to accelerate the inference of DBMs from 4x to
100x and even provide better generation quality than used teacher model
depending on particular setup.
A novel distillation method accelerates diffusion bridge models for image-to-image translation tasks, improving inference speed and quality.
diffusion bridge models inverse bridge matching distillation technique super-resolution JPEG restoration sketch-to-image image-to-image translation inference speed generation quality