title: T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with
    Trajectory Stitching
summary: Sampling from diffusion probabilistic models (DPMs) is often expensive for
  high-quality image generation and typically requires many steps with a large
  model. In this paper, we introduce sampling Trajectory Stitching T-Stitch, a
  simple yet efficient technique to improve the sampling efficiency with little
  or no generation degradation. Instead of solely using a large DPM for the
  entire sampling trajectory, T-Stitch first leverages a smaller DPM in the
  initial steps as a cheap drop-in replacement of the larger DPM and switches to
  the larger DPM at a later stage. Our key insight is that different diffusion
  models learn similar encodings under the same training data distribution and
  smaller models are capable of generating good global structures in the early
  steps. Extensive experiments demonstrate that T-Stitch is training-free,
  generally applicable for different architectures, and complements most existing
  fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL,
  for example, 40% of the early timesteps can be safely replaced with a 10x
  faster DiT-S without performance drop on class-conditional ImageNet generation.
  We further show that our method can also be used as a drop-in technique to not
  only accelerate the popular pretrained stable diffusion (SD) models but also
  improve the prompt alignment of stylized SD models from the public model zoo.
  Code is released at https://github.com/NVlabs/T-Stitch
paper_authors._id: 65d807403381a5be2777e499
paper_authors.name: Zizheng Pan
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paper_authors.fullname: Zizheng Pan
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paper_authors.name: Bohan Zhuang
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paper_authors.fullname: Bohan Zhuang
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paper_authors.name: De-An Huang
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paper_authors.fullname: De-An Huang
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paper_authors.name: Weili Nie
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paper_authors.fullname: Weili Nie
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paper_authors.user: xiaoli08
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paper_authors.name: Zhiding Yu
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paper_authors.fullname: Zhiding Yu
paper_authors.type: user
paper_authors.user: Zhiding
paper_authors._id: 65d807403381a5be2777e49e
paper_authors.name: Chaowei Xiao
paper_authors._id: 65d807403381a5be2777e49f
paper_authors.name: Jianfei Cai
paper_authors._id: 65d807403381a5be2777e4a0
paper_authors.name: Anima Anandkumar
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/cb9cc6d2733031582c83f56dc6cd1dd5.svg
paper_authors.fullname: Anima Anandkumar
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paper_authors.user: animakumar
paper_title: T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with
    Trajectory Stitching
paper_summary: Sampling from diffusion probabilistic models (DPMs) is often expensive for
  high-quality image generation and typically requires many steps with a large
  model. In this paper, we introduce sampling Trajectory Stitching T-Stitch, a
  simple yet efficient technique to improve the sampling efficiency with little
  or no generation degradation. Instead of solely using a large DPM for the
  entire sampling trajectory, T-Stitch first leverages a smaller DPM in the
  initial steps as a cheap drop-in replacement of the larger DPM and switches to
  the larger DPM at a later stage. Our key insight is that different diffusion
  models learn similar encodings under the same training data distribution and
  smaller models are capable of generating good global structures in the early
  steps. Extensive experiments demonstrate that T-Stitch is training-free,
  generally applicable for different architectures, and complements most existing
  fast sampling techniques with flexible speed and quality trade-offs. On DiT-XL,
  for example, 40% of the early timesteps can be safely replaced with a 10x
  faster DiT-S without performance drop on class-conditional ImageNet generation.
  We further show that our method can also be used as a drop-in technique to not
  only accelerate the popular pretrained stable diffusion (SD) models but also
  improve the prompt alignment of stylized SD models from the public model zoo.
  Code is released at https://github.com/NVlabs/T-Stitch
paper_ai_summary: T-Stitch, a technique for efficient sampling from diffusion probabilistic models, leverages smaller models early in the process to reduce computational cost without degrading image quality.
paper_ai_keywords: diffusion probabilistic models sampling Trajectory Stitching T-Stitch sampling efficiency diffusion models training-free fast sampling techniques DiT-XL DiT-S pretrained stable diffusion prompt alignment