title: Inference-Time Scaling for Flow Models via Stochastic Generation and
    Rollover Budget Forcing
summary: 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.
paper_authors._id: 67e36241d8da46951f858026
paper_authors.name: Jaihoon Kim
paper_authors._id: 67e36241d8da46951f858027
paper_authors.name: Taehoon Yoon
paper_authors._id: 67e36241d8da46951f858028
paper_authors.name: Jisung Hwang
paper_authors._id: 67e36241d8da46951f858029
paper_authors.name: Minhyuk Sung
paper_title: Inference-Time Scaling for Flow Models via Stochastic Generation and
    Rollover Budget Forcing
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: pretrained flow models inference-time scaling diffusion models particle sampling stochasticity denoising steps variance-preserving interpolant Rollover Budget Forcing