title: Training-free Diffusion Acceleration with Bottleneck Sampling
summary: Diffusion models have demonstrated remarkable capabilities in visual content
  generation but remain challenging to deploy due to their high computational
  cost during inference. This computational burden primarily arises from the
  quadratic complexity of self-attention with respect to image or video
  resolution. While existing acceleration methods often compromise output quality
  or necessitate costly retraining, we observe that most diffusion models are
  pre-trained at lower resolutions, presenting an opportunity to exploit these
  low-resolution priors for more efficient inference without degrading
  performance. In this work, we introduce Bottleneck Sampling, a training-free
  framework that leverages low-resolution priors to reduce computational overhead
  while preserving output fidelity. Bottleneck Sampling follows a high-low-high
  denoising workflow: it performs high-resolution denoising in the initial and
  final stages while operating at lower resolutions in intermediate steps. To
  mitigate aliasing and blurring artifacts, we further refine the resolution
  transition points and adaptively shift the denoising timesteps at each stage.
  We evaluate Bottleneck Sampling on both image and video generation tasks, where
  extensive experiments demonstrate that it accelerates inference by up to
  3times for image generation and 2.5times for video generation, all while
  maintaining output quality comparable to the standard full-resolution sampling
  process across multiple evaluation metrics. Code is available at:
  https://github.com/tyfeld/Bottleneck-Sampling
paper_authors._id: 67e21b305d20ec3277dac34a
paper_authors.name: Ye Tian
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paper_authors.name: Xin Xia
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paper_authors.name: Yuxi Ren
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paper_authors.name: Shanchuan Lin
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paper_authors.name: Xing Wang
paper_authors._id: 67e21b305d20ec3277dac34f
paper_authors.name: Xuefeng Xiao
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paper_authors.fullname: Xuefeng Xiao
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paper_authors._id: 67e21b305d20ec3277dac350
paper_authors.name: Yunhai Tong
paper_authors._id: 67e21b305d20ec3277dac351
paper_authors.name: Ling Yang
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paper_authors.fullname: Ling Yang
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paper_authors.user: Lingaaaaaaa
paper_authors._id: 67e21b305d20ec3277dac352
paper_authors.name: Bin Cui
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/d4346ac5a0ebbaeb828d832cc6ca9f0b.svg
paper_authors.fullname: Bin Cui
paper_authors.type: user
paper_authors.user: lazybone128
paper_title: Training-free Diffusion Acceleration with Bottleneck Sampling
paper_summary: Diffusion models have demonstrated remarkable capabilities in visual content
  generation but remain challenging to deploy due to their high computational
  cost during inference. This computational burden primarily arises from the
  quadratic complexity of self-attention with respect to image or video
  resolution. While existing acceleration methods often compromise output quality
  or necessitate costly retraining, we observe that most diffusion models are
  pre-trained at lower resolutions, presenting an opportunity to exploit these
  low-resolution priors for more efficient inference without degrading
  performance. In this work, we introduce Bottleneck Sampling, a training-free
  framework that leverages low-resolution priors to reduce computational overhead
  while preserving output fidelity. Bottleneck Sampling follows a high-low-high
  denoising workflow: it performs high-resolution denoising in the initial and
  final stages while operating at lower resolutions in intermediate steps. To
  mitigate aliasing and blurring artifacts, we further refine the resolution
  transition points and adaptively shift the denoising timesteps at each stage.
  We evaluate Bottleneck Sampling on both image and video generation tasks, where
  extensive experiments demonstrate that it accelerates inference by up to
  3times for image generation and 2.5times for video generation, all while
  maintaining output quality comparable to the standard full-resolution sampling
  process across multiple evaluation metrics. Code is available at:
  https://github.com/tyfeld/Bottleneck-Sampling
paper_ai_summary: Bottleneck Sampling accelerates diffusion model inference by using low-resolution priors while maintaining output fidelity in image and video generation.
paper_ai_keywords: diffusion models computational cost inference quadratic complexity self-attention low-resolution priors Bottleneck Sampling high-low-high denoising workflow resolution transition points adaptive shifting