Inference-Time Scaling for Diffusion Models beyond Scaling Denoising
  Steps
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.
6789e1dfaa9f64e4af498482
Nanye Ma
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2025-01-17T10:32:46.832Z
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Nanye Ma
user
willllis
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Shangyuan Tong
admin_assigned
2025-01-17T10:32:52.717Z
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Shangyuan Tong
user
sytong
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Haolin Jia
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Hexiang Hu
admin_assigned
2025-01-17T10:33:08.220Z
643441ccd55dea2d0ec2c309
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Hexiang Hu
user
hexianghu
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Yu-Chuan Su
admin_assigned
2025-01-17T10:33:14.078Z
6729508649696b4e066b0506
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Yu-Chuan Su
user
ycsu
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Mingda Zhang
admin_assigned
2025-01-17T10:33:35.040Z
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mingdazhang
user
mingdazhang
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Xuan Yang
6789e1dfaa9f64e4af498489
Yandong Li
admin_assigned
2025-01-17T10:33:47.975Z
6338914220fc636fd8b27fb8
/avatars/4c5a0c925c0f0296e02aa498218f339d.svg
li
user
yandong
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Tommi Jaakkola
6789e1dfaa9f64e4af49848b
Xuhui Jia
admin_assigned
2025-01-17T10:33:58.031Z
634ce7ce05f736dff37aae5f
/avatars/1405c76a38f7ea497e4439f0e4e786a8.svg
Xuhui Jia
user
Jxh-cuit
6789e1dfaa9f64e4af49848c
Saining Xie
admin_assigned
2025-01-17T10:34:03.297Z
6596422646624a86ff3b3bda
/avatars/216e12b77e45ac5f1fa20932f5745411.svg
Saining Xie
user
sainx
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising
  Steps
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.
Inference-time scaling of diffusion models improves sample quality beyond increasing denoising steps through better noise selection and verification.
diffusion models denoising steps inference-time scaling noise selection verification class-conditioned text-conditioned image generation