title: Differentiable Solver Search for Fast Diffusion Sampling
summary: Diffusion models have demonstrated remarkable generation quality but at the
  cost of numerous function evaluations. Recently, advanced ODE-based solvers
  have been developed to mitigate the substantial computational demands of
  reverse-diffusion solving under limited sampling steps. However, these solvers,
  heavily inspired by Adams-like multistep methods, rely solely on t-related
  Lagrange interpolation. We show that t-related Lagrange interpolation is
  suboptimal for diffusion model and reveal a compact search space comprised of
  time steps and solver coefficients. Building on our analysis, we propose a
  novel differentiable solver search algorithm to identify more optimal solver.
  Equipped with the searched solver, rectified-flow models, e.g., SiT-XL/2 and
  FlowDCN-XL/2, achieve FID scores of 2.40 and 2.35, respectively, on ImageNet256
  with only 10 steps. Meanwhile, DDPM model, DiT-XL/2, reaches a FID score of
  2.33 with only 10 steps. Notably, our searched solver outperforms traditional
  solvers by a significant margin. Moreover, our searched solver demonstrates
  generality across various model architectures, resolutions, and model sizes.
paper_authors._id: 68366f4410fa22cd420ae295
paper_authors.name: Shuai Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:12.608Z
paper_authors._id: 66615c855fd9d736e670e0a9
paper_authors.avatarUrl: /avatars/0ff3127b513552432a7c651e21d7f283.svg
paper_authors.fullname: wangshuai
paper_authors.type: user
paper_authors.user: wangsssssss
paper_authors._id: 68366f4410fa22cd420ae296
paper_authors.name: Zexian Li
paper_authors._id: 68366f4410fa22cd420ae297
paper_authors.name: Qipeng zhang
paper_authors._id: 68366f4410fa22cd420ae298
paper_authors.name: Tianhui Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:10.255Z
paper_authors._id: 649e7693a83143427691769c
paper_authors.avatarUrl: /avatars/d04f7b3d417423abaa053375212da21f.svg
paper_authors.fullname: Tianhui Song
paper_authors.type: user
paper_authors.user: sthuihui
paper_authors._id: 68366f4410fa22cd420ae299
paper_authors.name: Xubin Li
paper_authors._id: 68366f4410fa22cd420ae29a
paper_authors.name: Tiezheng Ge
paper_authors._id: 68366f4410fa22cd420ae29b
paper_authors.name: Bo Zheng
paper_authors._id: 68366f4410fa22cd420ae29c
paper_authors.name: Limin Wang
paper_title: Differentiable Solver Search for Fast Diffusion Sampling
paper_summary: Diffusion models have demonstrated remarkable generation quality but at the
  cost of numerous function evaluations. Recently, advanced ODE-based solvers
  have been developed to mitigate the substantial computational demands of
  reverse-diffusion solving under limited sampling steps. However, these solvers,
  heavily inspired by Adams-like multistep methods, rely solely on t-related
  Lagrange interpolation. We show that t-related Lagrange interpolation is
  suboptimal for diffusion model and reveal a compact search space comprised of
  time steps and solver coefficients. Building on our analysis, we propose a
  novel differentiable solver search algorithm to identify more optimal solver.
  Equipped with the searched solver, rectified-flow models, e.g., SiT-XL/2 and
  FlowDCN-XL/2, achieve FID scores of 2.40 and 2.35, respectively, on ImageNet256
  with only 10 steps. Meanwhile, DDPM model, DiT-XL/2, reaches a FID score of
  2.33 with only 10 steps. Notably, our searched solver outperforms traditional
  solvers by a significant margin. Moreover, our searched solver demonstrates
  generality across various model architectures, resolutions, and model sizes.
paper_ai_summary: Researchers propose a novel differentiable solver search algorithm that optimizes the computational efficiency and quality of diffusion models for image generation tasks.
paper_ai_keywords: diffusion models ODE-based solvers Adams-like multistep methods t-related Lagrange interpolation differentiable solver search algorithm rectified-flow models SiT-XL/2 FlowDCN-XL/2 DDPM DiT-XL/2 FID scores ImageNet256 computational efficiency generality