Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing

• URL: http://arxiv.org/abs/2503.19385v2
• Date: Wed, 26 Mar 2025 12:12:38 GMT
• Title: Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing
• Authors: Jaihoon Kim, Taehoon Yoon, Jisung Hwang, Minhyuk Sung,
• Abstract summary: We propose an inference-time scaling approach for pretrained flow models.<n>We show that SDE-based generation, particularly variance-preserving (VP) interpolant-based generation, improves particle sampling methods for inference-time scaling in flow models.
• Score: 10.542645300983878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment [12.823734370183482]
  We introduce DDIM-InPO, an efficient method for direct preference alignment of diffusion models.<n>Our approach conceptualizes diffusion model as a single-step generative model, allowing us to fine-tune the outputs of specific latent variables selectively.<n> Experimental results indicate that our DDIM-InPO achieves state-of-the-art performance with just 400 steps of fine-tuning.
  arXiv  Detail & Related papers  (2025-03-24T08:58:49Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization [97.35427957922714]
  We present an algorithm named pairwise sample optimization (PSO), which enables the direct fine-tuning of an arbitrary timestep-distilled diffusion model. PSO introduces additional reference images sampled from the current time-step distilled model, and increases the relative likelihood margin between the training images and reference images. We show that PSO can directly adapt distilled models to human-preferred generation with both offline and online-generated pairwise preference image data.
  arXiv  Detail & Related papers  (2024-10-04T07:05:16Z)
• Text-to-Image Rectified Flow as Plug-and-Play Priors [52.586838532560755]
  Rectified flow is a novel class of generative models that enforces a linear progression from the source to the target distribution.<n>We show that rectified flow approaches surpass in terms of generation quality and efficiency, requiring fewer inference steps.<n>Our method also displays competitive performance in image inversion and editing.
  arXiv  Detail & Related papers  (2024-06-05T14:02:31Z)
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process [26.661721555671626]
  We introduce a novel Multi-Granularity Time Series (MG-TSD) model, which achieves state-of-the-art predictive performance. Our approach does not rely on additional external data, making it versatile and applicable across various domains.
  arXiv  Detail & Related papers  (2024-03-09T01:15:03Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• Memory-Efficient Fine-Tuning for Quantized Diffusion Model [12.875837358532422]
  We introduce TuneQDM, a memory-efficient fine-tuning method for quantized diffusion models. Our method consistently outperforms the baseline in both single-/multi-subject generations.
  arXiv  Detail & Related papers  (2024-01-09T03:42:08Z)
• Guided Flows for Generative Modeling and Decision Making [55.42634941614435]
  We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text synthesis-to-speech. Notably, we are first to apply flow models for plan generation in the offline reinforcement learning setting ax speedup in compared to diffusion models.
  arXiv  Detail & Related papers  (2023-11-22T15:07:59Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation [32.74923906921339]
  Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application is hampered by their inherently slow generation speed. We propose AdaDiff, an adaptive framework that dynamically allocates computation resources in each sampling step to improve the generation efficiency of diffusion models.
  arXiv  Detail & Related papers  (2023-09-29T09:10:04Z)
• A prior regularized full waveform inversion using generative diffusion models [0.5156484100374059]
  Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. Due to limitations in observation, e.g., regional noise, limited shots or receivers, and band-limited data, it is hard to obtain the desired high-resolution model with FWI. We propose a new paradigm for FWI regularized by generative diffusion models.
  arXiv  Detail & Related papers  (2023-06-22T10:10:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.