title: Inverse Bridge Matching Distillation
summary: 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.
paper_authors._id: 67a2ad6ac7caec9bf5a45e61
paper_authors.name: Nikita Gushchin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-05T10:14:26.177Z
paper_authors._id: 672503c59f68afdd63cc81a2
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/672503c59f68afdd63cc81a2/lw4ApCTwAKgt_uUyfSVRH.jpeg
paper_authors.fullname: Nikita Gushchin
paper_authors.type: user
paper_authors.user: ngushchin
paper_authors._id: 67a2ad6ac7caec9bf5a45e62
paper_authors.name: David Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-05T10:14:24.236Z
paper_authors._id: 656a2e59b4020389028dc85f
paper_authors.avatarUrl: /avatars/6fda3bddc3cecba2894233bebb3de968.svg
paper_authors.fullname: David Li
paper_authors.type: user
paper_authors.user: kekchpek
paper_authors._id: 67a2ad6ac7caec9bf5a45e63
paper_authors.name: Daniil Selikhanovych
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-05T10:17:09.668Z
paper_authors._id: 64a42977250bfdecd9570a9e
paper_authors.avatarUrl: /avatars/df5d7cf159e6bb9e961e1c77d1b89d36.svg
paper_authors.fullname: Daniil Selikhanovych
paper_authors.type: user
paper_authors.user: apryc1
paper_authors._id: 67a2ad6ac7caec9bf5a45e64
paper_authors.name: Evgeny Burnaev
paper_authors._id: 67a2ad6ac7caec9bf5a45e65
paper_authors.name: Dmitry Baranchuk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-05T10:17:26.518Z
paper_authors._id: 62b6cc49752323892323bc04
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b6cc49752323892323bc04/gGBld1KJIP9AIpd81L3PC.jpeg
paper_authors.fullname: Dmitry Baranchuk
paper_authors.type: user
paper_authors.user: dbaranchuk
paper_authors._id: 67a2ad6ac7caec9bf5a45e66
paper_authors.name: Alexander Korotin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-05T10:17:33.816Z
paper_authors._id: 67a31c9ae5b870d5157657db
paper_authors.avatarUrl: /avatars/ca5fd356e3656e1beacb5a28ecaad5be.svg
paper_authors.fullname: Alexander Korotin
paper_authors.type: user
paper_authors.user: akorotin
paper_title: Inverse Bridge Matching Distillation
paper_summary: 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.
paper_ai_keywords: diffusion bridge models (DBMs) diffusion models image-to-image translation inverse bridge matching formulation distillation technique conditional DBMs unconditional DBMs corrupted images super-resolution JPEG restoration sketch-to-image