title: Inference-Time Scaling for Diffusion Models beyond Scaling Denoising
    Steps
summary: Generative models have made significant impacts across various domains,
  largely due to their ability to scale during training by increasing data,
  computational resources, and model size, a phenomenon characterized by the
  scaling laws. Recent research has begun to explore inference-time scaling
  behavior in Large Language Models (LLMs), revealing how performance can further
  improve with additional computation during inference. Unlike LLMs, diffusion
  models inherently possess the flexibility to adjust inference-time computation
  via the number of denoising steps, although the performance gains typically
  flatten after a few dozen. In this work, we explore the inference-time scaling
  behavior of diffusion models beyond increasing denoising steps and investigate
  how the generation performance can further improve with increased computation.
  Specifically, we consider a search problem aimed at identifying better noises
  for the diffusion sampling process. We structure the design space along two
  axes: the verifiers used to provide feedback, and the algorithms used to find
  better noise candidates. Through extensive experiments on class-conditioned and
  text-conditioned image generation benchmarks, our findings reveal that
  increasing inference-time compute leads to substantial improvements in the
  quality of samples generated by diffusion models, and with the complicated
  nature of images, combinations of the components in the framework can be
  specifically chosen to conform with different application scenario.
paper_authors._id: 6789e1dfaa9f64e4af498482
paper_authors.name: Nanye Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:32:46.832Z
paper_authors._id: 66c398fc5f4422f886b71a00
paper_authors.avatarUrl: /avatars/9cd690d7857de1b926ddcdc2bccbfdfa.svg
paper_authors.fullname: Nanye Ma
paper_authors.type: user
paper_authors.user: willllis
paper_authors._id: 6789e1dfaa9f64e4af498483
paper_authors.name: Shangyuan Tong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:32:52.717Z
paper_authors._id: 64d39e0f5ceebf9c30359082
paper_authors.avatarUrl: /avatars/7c21f18874498a793dd2275277d4dafb.svg
paper_authors.fullname: Shangyuan Tong
paper_authors.type: user
paper_authors.user: sytong
paper_authors._id: 6789e1dfaa9f64e4af498484
paper_authors.name: Haolin Jia
paper_authors._id: 6789e1dfaa9f64e4af498485
paper_authors.name: Hexiang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:33:08.220Z
paper_authors._id: 643441ccd55dea2d0ec2c309
paper_authors.avatarUrl: /avatars/82e99d445e2b513ad7270fa852adbcbb.svg
paper_authors.fullname: Hexiang Hu
paper_authors.type: user
paper_authors.user: hexianghu
paper_authors._id: 6789e1dfaa9f64e4af498486
paper_authors.name: Yu-Chuan Su
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:33:14.078Z
paper_authors._id: 6729508649696b4e066b0506
paper_authors.avatarUrl: /avatars/246e13b236edf039f2ca27e4f4051be8.svg
paper_authors.fullname: Yu-Chuan Su
paper_authors.type: user
paper_authors.user: ycsu
paper_authors._id: 6789e1dfaa9f64e4af498487
paper_authors.name: Mingda Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:33:35.040Z
paper_authors._id: 65676b1711b2bbd6c2ab093a
paper_authors.avatarUrl: /avatars/bdd4364057c6b9e54d7ec451ad1ffb64.svg
paper_authors.fullname: mingdazhang
paper_authors.type: user
paper_authors.user: mingdazhang
paper_authors._id: 6789e1dfaa9f64e4af498488
paper_authors.name: Xuan Yang
paper_authors._id: 6789e1dfaa9f64e4af498489
paper_authors.name: Yandong Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:33:47.975Z
paper_authors._id: 6338914220fc636fd8b27fb8
paper_authors.avatarUrl: /avatars/4c5a0c925c0f0296e02aa498218f339d.svg
paper_authors.fullname: li
paper_authors.type: user
paper_authors.user: yandong
paper_authors._id: 6789e1dfaa9f64e4af49848a
paper_authors.name: Tommi Jaakkola
paper_authors._id: 6789e1dfaa9f64e4af49848b
paper_authors.name: Xuhui Jia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:33:58.031Z
paper_authors._id: 634ce7ce05f736dff37aae5f
paper_authors.avatarUrl: /avatars/1405c76a38f7ea497e4439f0e4e786a8.svg
paper_authors.fullname: Xuhui Jia
paper_authors.type: user
paper_authors.user: Jxh-cuit
paper_authors._id: 6789e1dfaa9f64e4af49848c
paper_authors.name: Saining Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:34:03.297Z
paper_authors._id: 6596422646624a86ff3b3bda
paper_authors.avatarUrl: /avatars/216e12b77e45ac5f1fa20932f5745411.svg
paper_authors.fullname: Saining Xie
paper_authors.type: user
paper_authors.user: sainx
paper_title: Inference-Time Scaling for Diffusion Models beyond Scaling Denoising
    Steps
paper_summary: Generative models have made significant impacts across various domains,
  largely due to their ability to scale during training by increasing data,
  computational resources, and model size, a phenomenon characterized by the
  scaling laws. Recent research has begun to explore inference-time scaling
  behavior in Large Language Models (LLMs), revealing how performance can further
  improve with additional computation during inference. Unlike LLMs, diffusion
  models inherently possess the flexibility to adjust inference-time computation
  via the number of denoising steps, although the performance gains typically
  flatten after a few dozen. In this work, we explore the inference-time scaling
  behavior of diffusion models beyond increasing denoising steps and investigate
  how the generation performance can further improve with increased computation.
  Specifically, we consider a search problem aimed at identifying better noises
  for the diffusion sampling process. We structure the design space along two
  axes: the verifiers used to provide feedback, and the algorithms used to find
  better noise candidates. Through extensive experiments on class-conditioned and
  text-conditioned image generation benchmarks, our findings reveal that
  increasing inference-time compute leads to substantial improvements in the
  quality of samples generated by diffusion models, and with the complicated
  nature of images, combinations of the components in the framework can be
  specifically chosen to conform with different application scenario.
paper_ai_keywords: generative models Large Language Models (LLMs) diffusion models denoising steps diffusion performance inference-time behavior noise candidates class-conditioned image generation text-conditioned image generation diffusion sampling process performance improvements image quality