Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
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.
64bdd85d78b89c4aa406656b
Suttisak Wizadwongsa
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2023-07-25T08:08:01.461Z
630446621dd5d3c62488096f
/avatars/df6a828e2644948de3391447a595171a.svg
Suttisak Wizadongsa
user
sWizad
64bdd85d78b89c4aa406656c
Worameth Chinchuthakun
admin_assigned
2023-07-24T07:59:58.802Z
646c28551da1b6d027fecc32
/avatars/eeca1f751db95c358614d7177b72f955.svg
Worameth Chinchuthakun
user
sincostanx
64bdd85d78b89c4aa406656d
Pramook Khungurn
64bdd85d78b89c4aa406656e
Amit Raj
64bdd85d78b89c4aa406656f
Supasorn Suwajanakorn
claimed_verified
2023-07-24T11:31:35.995Z
64be43eb4561d0aca2b66714
/avatars/ea76326e73cacdb9e7ee00d0fe29f457.svg
Supasorn Suwajanakorn
user
supasorn
Diffusion Sampling with Momentum for Mitigating Divergence Artifacts
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.
Proposed Heavy Ball and Generalized Heavy Ball momentum techniques improve numerical stability and reduce artifacts in diffusion model sampling, enhancing image quality.
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