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October 20, 2025Open Access

GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models

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GLASS Flows eliminates trade-offs between stochastic evolution and efficiency in flow and diffusion models.
The introduction of an inner flow matching model enhances sampling without the need for re-training existing models.
This method combines efficiency of ODE with stochastic evolution of SDE, resulting in superior performance.
GLASS Flows, integrated with Feynman-Kac Steering, sets new benchmarks in text-to-image generation.

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GLASS Flows enhances flow matching and diffusion models in text-to-image generation, indicating improved efficiency and performance.

Holderrieth et al. (2025) studied this question.

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