Inference-Time Scaling for Flow Models via Stochastic Generation and
  Rollover Budget Forcing
We propose an inference-time scaling approach for pretrained flow models.
Recently, inference-time scaling has gained significant attention in LLMs and
diffusion models, improving sample quality or better aligning outputs with user
preferences by leveraging additional computation. For diffusion models,
particle sampling has allowed more efficient scaling due to the stochasticity
at intermediate denoising steps. On the contrary, while flow models have gained
popularity as an alternative to diffusion models--offering faster generation
and high-quality outputs in state-of-the-art image and video generative
models--efficient inference-time scaling methods used for diffusion models
cannot be directly applied due to their deterministic generative process. To
enable efficient inference-time scaling for flow models, we propose three key
ideas: 1) SDE-based generation, enabling particle sampling in flow models, 2)
Interpolant conversion, broadening the search space and enhancing sample
diversity, and 3) Rollover Budget Forcing (RBF), an adaptive allocation of
computational resources across timesteps to maximize budget utilization. Our
experiments show that SDE-based generation, particularly variance-preserving
(VP) interpolant-based generation, improves the performance of particle
sampling methods for inference-time scaling in flow models. Additionally, we
demonstrate that RBF with VP-SDE achieves the best performance, outperforming
all previous inference-time scaling approaches.
67e36241d8da46951f858026
Jaihoon Kim
67e36241d8da46951f858027
Taehoon Yoon
67e36241d8da46951f858028
Jisung Hwang
67e36241d8da46951f858029
Minhyuk Sung
Inference-Time Scaling for Flow Models via Stochastic Generation and
  Rollover Budget Forcing
We propose an inference-time scaling approach for pretrained flow models.
Recently, inference-time scaling has gained significant attention in LLMs and
diffusion models, improving sample quality or better aligning outputs with user
preferences by leveraging additional computation. For diffusion models,
particle sampling has allowed more efficient scaling due to the stochasticity
at intermediate denoising steps. On the contrary, while flow models have gained
popularity as an alternative to diffusion models--offering faster generation
and high-quality outputs in state-of-the-art image and video generative
models--efficient inference-time scaling methods used for diffusion models
cannot be directly applied due to their deterministic generative process. To
enable efficient inference-time scaling for flow models, we propose three key
ideas: 1) SDE-based generation, enabling particle sampling in flow models, 2)
Interpolant conversion, broadening the search space and enhancing sample
diversity, and 3) Rollover Budget Forcing (RBF), an adaptive allocation of
computational resources across timesteps to maximize budget utilization. Our
experiments show that SDE-based generation, particularly variance-preserving
(VP) interpolant-based generation, improves the performance of particle
sampling methods for inference-time scaling in flow models. Additionally, we
demonstrate that RBF with VP-SDE achieves the best performance, outperforming
all previous inference-time scaling approaches.
A new method enables efficient inference-time scaling for flow models through SDE-based generation, interpolant conversion, and Rollover Budget Forcing, improving sample quality and diversity.
pretrained flow models inference-time scaling diffusion models particle sampling stochasticity denoising steps variance-preserving interpolant Rollover Budget Forcing