title: Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
summary: Despite the remarkable success of diffusion models in image generation, slow
  sampling remains a persistent issue. To accelerate the sampling process, prior
  studies have reformulated diffusion sampling as an ODE/SDE and introduced
  higher-order numerical methods. However, these methods often produce divergence
  artifacts, especially with a low number of sampling steps, which limits the
  achievable acceleration. In this paper, we investigate the potential causes of
  these artifacts and suggest that the small stability regions of these methods
  could be the principal cause. To address this issue, we propose two novel
  techniques. The first technique involves the incorporation of Heavy Ball (HB)
  momentum, a well-known technique for improving optimization, into existing
  diffusion numerical methods to expand their stability regions. We also prove
  that the resulting methods have first-order convergence. The second technique,
  called Generalized Heavy Ball (GHVB), constructs a new high-order method that
  offers a variable trade-off between accuracy and artifact suppression.
  Experimental results show that our techniques are highly effective in reducing
  artifacts and improving image quality, surpassing state-of-the-art diffusion
  solvers on both pixel-based and latent-based diffusion models for low-step
  sampling. Our research provides novel insights into the design of numerical
  methods for future diffusion work.
paper_authors._id: 64bdd85d78b89c4aa406656b
paper_authors.name: Suttisak Wizadwongsa
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-25T08:08:01.461Z
paper_authors._id: 630446621dd5d3c62488096f
paper_authors.avatarUrl: /avatars/df6a828e2644948de3391447a595171a.svg
paper_authors.fullname: Suttisak Wizadongsa
paper_authors.type: user
paper_authors.user: sWizad
paper_authors._id: 64bdd85d78b89c4aa406656c
paper_authors.name: Worameth Chinchuthakun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-24T07:59:58.802Z
paper_authors._id: 646c28551da1b6d027fecc32
paper_authors.avatarUrl: /avatars/eeca1f751db95c358614d7177b72f955.svg
paper_authors.fullname: Worameth Chinchuthakun
paper_authors.type: user
paper_authors.user: sincostanx
paper_authors._id: 64bdd85d78b89c4aa406656d
paper_authors.name: Pramook Khungurn
paper_authors._id: 64bdd85d78b89c4aa406656e
paper_authors.name: Amit Raj
paper_authors._id: 64bdd85d78b89c4aa406656f
paper_authors.name: Supasorn Suwajanakorn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-24T11:31:35.995Z
paper_authors._id: 64be43eb4561d0aca2b66714
paper_authors.avatarUrl: /avatars/ea76326e73cacdb9e7ee00d0fe29f457.svg
paper_authors.fullname: Supasorn Suwajanakorn
paper_authors.type: user
paper_authors.user: supasorn
paper_title: Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
paper_summary: Despite the remarkable success of diffusion models in image generation, slow
  sampling remains a persistent issue. To accelerate the sampling process, prior
  studies have reformulated diffusion sampling as an ODE/SDE and introduced
  higher-order numerical methods. However, these methods often produce divergence
  artifacts, especially with a low number of sampling steps, which limits the
  achievable acceleration. In this paper, we investigate the potential causes of
  these artifacts and suggest that the small stability regions of these methods
  could be the principal cause. To address this issue, we propose two novel
  techniques. The first technique involves the incorporation of Heavy Ball (HB)
  momentum, a well-known technique for improving optimization, into existing
  diffusion numerical methods to expand their stability regions. We also prove
  that the resulting methods have first-order convergence. The second technique,
  called Generalized Heavy Ball (GHVB), constructs a new high-order method that
  offers a variable trade-off between accuracy and artifact suppression.
  Experimental results show that our techniques are highly effective in reducing
  artifacts and improving image quality, surpassing state-of-the-art diffusion
  solvers on both pixel-based and latent-based diffusion models for low-step
  sampling. Our research provides novel insights into the design of numerical
  methods for future diffusion work.
paper_ai_summary: Proposed Heavy Ball and Generalized Heavy Ball momentum techniques improve numerical stability and reduce artifacts in diffusion model sampling, enhancing image quality.
paper_ai_keywords: diffusion models ODE SDE higher-order numerical methods divergence artifacts stability regions Heavy Ball momentum first-order convergence Generalized Heavy Ball pixel-based diffusion models latent-based diffusion models low-step sampling