Differentiable Solver Search for Fast Diffusion Sampling
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.
68366f4410fa22cd420ae295
Shuai Wang
claimed_verified
2025-05-28T08:58:12.608Z
66615c855fd9d736e670e0a9
/avatars/0ff3127b513552432a7c651e21d7f283.svg
wangshuai
user
wangsssssss
68366f4410fa22cd420ae296
Zexian Li
68366f4410fa22cd420ae297
Qipeng zhang
68366f4410fa22cd420ae298
Tianhui Song
claimed_verified
2025-05-28T08:58:10.255Z
649e7693a83143427691769c
/avatars/d04f7b3d417423abaa053375212da21f.svg
Tianhui Song
user
sthuihui
68366f4410fa22cd420ae299
Xubin Li
68366f4410fa22cd420ae29a
Tiezheng Ge
68366f4410fa22cd420ae29b
Bo Zheng
68366f4410fa22cd420ae29c
Limin Wang
Differentiable Solver Search for Fast Diffusion Sampling
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.
Researchers propose a novel differentiable solver search algorithm that optimizes the computational efficiency and quality of diffusion models for image generation tasks.
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