title: Gaussian Mixture Flow Matching Models
summary: Diffusion models approximate the denoising distribution as a Gaussian and
  predict its mean, whereas flow matching models reparameterize the Gaussian mean
  as flow velocity. However, they underperform in few-step sampling due to
  discretization error and tend to produce over-saturated colors under
  classifier-free guidance (CFG). To address these limitations, we propose a
  novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the
  mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a
  multi-modal flow velocity distribution, which can be learned with a KL
  divergence loss. We demonstrate that GMFlow generalizes previous diffusion and
  flow matching models where a single Gaussian is learned with an L_2 denoising
  loss. For inference, we derive GM-SDE/ODE solvers that leverage analytic
  denoising distributions and velocity fields for precise few-step sampling.
  Furthermore, we introduce a novel probabilistic guidance scheme that mitigates
  the over-saturation issues of CFG and improves image generation quality.
  Extensive experiments demonstrate that GMFlow consistently outperforms flow
  matching baselines in generation quality, achieving a Precision of 0.942 with
  only 6 sampling steps on ImageNet 256times256.
paper_authors._id: 67f48c11412c65a9d4e3e552
paper_authors.name: Hansheng Chen
paper_authors.status: claimed_verified
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paper_authors.fullname: Hansheng Chen
paper_authors.type: user
paper_authors.user: Lakonik
paper_authors._id: 67f48c11412c65a9d4e3e553
paper_authors.name: Kai Zhang
paper_authors._id: 67f48c11412c65a9d4e3e554
paper_authors.name: Hao Tan
paper_authors._id: 67f48c11412c65a9d4e3e555
paper_authors.name: Zexiang Xu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/c245b19ed05292bf1bf4a9d8cf2a409c.svg
paper_authors.fullname: Zexiang Xu
paper_authors.type: user
paper_authors.user: zexiangxu
paper_authors._id: 67f48c11412c65a9d4e3e556
paper_authors.name: Fujun Luan
paper_authors.status: admin_assigned
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paper_authors.fullname: Fujun Luan
paper_authors.type: user
paper_authors.user: luanfujun
paper_authors._id: 67f48c11412c65a9d4e3e557
paper_authors.name: Leonidas Guibas
paper_authors._id: 67f48c11412c65a9d4e3e558
paper_authors.name: Gordon Wetzstein
paper_authors.status: admin_assigned
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paper_authors.fullname: Gordon Wetzstein
paper_authors.type: user
paper_authors.user: wetzste1
paper_authors._id: 67f48c11412c65a9d4e3e559
paper_authors.name: Sai Bi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-08T10:38:03.075Z
paper_authors._id: 66303560597dc7c535168f07
paper_authors.avatarUrl: /avatars/a69d52a4a198c6c8451c1d137031bf07.svg
paper_authors.fullname: Sai Bi
paper_authors.type: user
paper_authors.user: saibi
paper_title: Gaussian Mixture Flow Matching Models
paper_summary: Diffusion models approximate the denoising distribution as a Gaussian and
  predict its mean, whereas flow matching models reparameterize the Gaussian mean
  as flow velocity. However, they underperform in few-step sampling due to
  discretization error and tend to produce over-saturated colors under
  classifier-free guidance (CFG). To address these limitations, we propose a
  novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the
  mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a
  multi-modal flow velocity distribution, which can be learned with a KL
  divergence loss. We demonstrate that GMFlow generalizes previous diffusion and
  flow matching models where a single Gaussian is learned with an L_2 denoising
  loss. For inference, we derive GM-SDE/ODE solvers that leverage analytic
  denoising distributions and velocity fields for precise few-step sampling.
  Furthermore, we introduce a novel probabilistic guidance scheme that mitigates
  the over-saturation issues of CFG and improves image generation quality.
  Extensive experiments demonstrate that GMFlow consistently outperforms flow
  matching baselines in generation quality, achieving a Precision of 0.942 with
  only 6 sampling steps on ImageNet 256times256.
paper_ai_summary: A Gaussian mixture flow matching model addresses few-step sampling errors and over-saturation in diffusion and flow matching models, improving image generation quality.
paper_ai_keywords: diffusion models flow matching models Gaussian distribution flow velocity dynamic Gaussian mixture KL divergence loss GM-SDE/ODE solvers classifier-free guidance GMFlow