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arXiv · 2404.14161

Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion

Dohoon Lee·Jaehyun Park·Hyunwoo J. Kim·Kyogu Lee
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Abstract

Flow and diffusion models have demonstrated strong performance and training stability across various tasks but lack two critical properties of simulation-based methods: freedom of dimensionality and adaptability to different inference trajectories. To address this limitation, we propose the Multidimensional Adaptive Coefficient (MAC), a plug-in module for flow and diffusion models that extends conventional unidimensional coefficients to multidimensional ones and enables inference trajectory-wise adaptation. MAC is trained via simulation-based feedback through adversarial refinement. Empirical results across diverse frameworks and datasets demonstrate that MAC enhances generative quality with high training efficiency. Consequently, our work offers a new perspective on inference trajectory optimality, encouraging future research to move beyond vector field design and to leverage training-efficient, simulation-based optimization.

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Dohoon Lee, Jaehyun Park, Hyunwoo J. Kim, Kyogu Lee. 2024-04-22. Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion. https://arxiv.org/abs/2404.14161

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