title: Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint
    Method
summary: Training-free guided sampling in diffusion models leverages off-the-shelf
  pre-trained networks, such as an aesthetic evaluation model, to guide the
  generation process. Current training-free guided sampling algorithms obtain the
  guidance energy function based on a one-step estimate of the clean image.
  However, since the off-the-shelf pre-trained networks are trained on clean
  images, the one-step estimation procedure of the clean image may be inaccurate,
  especially in the early stages of the generation process in diffusion models.
  This causes the guidance in the early time steps to be inaccurate. To overcome
  this problem, we propose Symplectic Adjoint Guidance (SAG), which calculates
  the gradient guidance in two inner stages. Firstly, SAG estimates the clean
  image via n function calls, where n serves as a flexible hyperparameter
  that can be tailored to meet specific image quality requirements. Secondly, SAG
  uses the symplectic adjoint method to obtain the gradients accurately and
  efficiently in terms of the memory requirements. Extensive experiments
  demonstrate that SAG generates images with higher qualities compared to the
  baselines in both guided image and video generation tasks.
paper_authors._id: 65824fbf083a345666a6bbb7
paper_authors.name: Jiachun Pan
paper_authors._id: 65824fbf083a345666a6bbb8
paper_authors.name: Hanshu Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:47:21.401Z
paper_authors._id: 631eec57daa9591e522ee721
paper_authors.avatarUrl: /avatars/39a6fd3b9717c142b73be8c81c69ebe5.svg
paper_authors.fullname: PeRFlow
paper_authors.type: user
paper_authors.user: hansyan
paper_authors._id: 65824fbf083a345666a6bbb9
paper_authors.name: Jun Hao Liew
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-20T09:47:28.087Z
paper_authors._id: 6305bb149d2531fabd198638
paper_authors.avatarUrl: /avatars/0ec3c55d445264d43c0430f9edf88bf8.svg
paper_authors.fullname: Jun Hao Liew
paper_authors.type: user
paper_authors.user: junhao910323
paper_authors._id: 65824fbf083a345666a6bbba
paper_authors.name: Jiashi Feng
paper_authors._id: 65824fbf083a345666a6bbbb
paper_authors.name: Vincent Y. F. Tan
paper_title: Towards Accurate Guided Diffusion Sampling through Symplectic Adjoint
    Method
paper_summary: Training-free guided sampling in diffusion models leverages off-the-shelf
  pre-trained networks, such as an aesthetic evaluation model, to guide the
  generation process. Current training-free guided sampling algorithms obtain the
  guidance energy function based on a one-step estimate of the clean image.
  However, since the off-the-shelf pre-trained networks are trained on clean
  images, the one-step estimation procedure of the clean image may be inaccurate,
  especially in the early stages of the generation process in diffusion models.
  This causes the guidance in the early time steps to be inaccurate. To overcome
  this problem, we propose Symplectic Adjoint Guidance (SAG), which calculates
  the gradient guidance in two inner stages. Firstly, SAG estimates the clean
  image via n function calls, where n serves as a flexible hyperparameter
  that can be tailored to meet specific image quality requirements. Secondly, SAG
  uses the symplectic adjoint method to obtain the gradients accurately and
  efficiently in terms of the memory requirements. Extensive experiments
  demonstrate that SAG generates images with higher qualities compared to the
  baselines in both guided image and video generation tasks.
paper_ai_summary: Symplectic Adjoint Guidance (SAG) enhances the accuracy and quality of generated images and videos by refining clean image estimation and computing gradients efficiently in diffusion models.
paper_ai_keywords: diffusion models guided sampling aesthetic evaluation model guidance energy function symplectic adjoint method image generation video generation