Gaussian Mixture Flow Matching Models
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.
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Hansheng Chen
claimed_verified
2025-04-08T06:51:39.675Z
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Hansheng Chen
user
Lakonik
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Kai Zhang
67f48c11412c65a9d4e3e554
Hao Tan
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Zexiang Xu
admin_assigned
2025-04-08T10:38:32.849Z
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Zexiang Xu
user
zexiangxu
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Fujun Luan
admin_assigned
2025-04-08T10:38:25.360Z
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Fujun Luan
user
luanfujun
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Leonidas Guibas
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Gordon Wetzstein
admin_assigned
2025-04-08T10:38:08.838Z
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Gordon Wetzstein
user
wetzste1
67f48c11412c65a9d4e3e559
Sai Bi
admin_assigned
2025-04-08T10:38:03.075Z
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/avatars/a69d52a4a198c6c8451c1d137031bf07.svg
Sai Bi
user
saibi
Gaussian Mixture Flow Matching Models
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.
A Gaussian mixture flow matching model addresses few-step sampling errors and over-saturation in diffusion and flow matching models, improving image generation quality.
diffusion models flow matching models Gaussian distribution flow velocity dynamic Gaussian mixture KL divergence loss GM-SDE/ODE solvers classifier-free guidance GMFlow