MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE
  Solvers
In applications of diffusion models, controllable generation is of practical
significance, but is also challenging. Current methods for controllable
generation primarily focus on modifying the score function of diffusion models,
while Mean Reverting (MR) Diffusion directly modifies the structure of the
stochastic differential equation (SDE), making the incorporation of image
conditions simpler and more natural. However, current training-free fast
samplers are not directly applicable to MR Diffusion. And thus MR Diffusion
requires hundreds of NFEs (number of function evaluations) to obtain
high-quality samples. In this paper, we propose a new algorithm named MRS (MR
Sampler) to reduce the sampling NFEs of MR Diffusion. We solve the reverse-time
SDE and the probability flow ordinary differential equation (PF-ODE) associated
with MR Diffusion, and derive semi-analytical solutions. The solutions consist
of an analytical function and an integral parameterized by a neural network.
Based on this solution, we can generate high-quality samples in fewer steps.
Our approach does not require training and supports all mainstream
parameterizations, including noise prediction, data prediction and velocity
prediction. Extensive experiments demonstrate that MR Sampler maintains high
sampling quality with a speedup of 10 to 20 times across ten different image
restoration tasks. Our algorithm accelerates the sampling procedure of MR
Diffusion, making it more practical in controllable generation.
67b2dedc8a276e7b485a9bcd
Ao Li
claimed_verified
2025-03-12T20:46:19.025Z
64fe6f5958718aaa93264e62
/avatars/75688072f466723dd5f5dba8cdd7536a.svg
Ao Li
user
321rf13
67b2dedc8a276e7b485a9bce
Wei Fang
67b2dedc8a276e7b485a9bcf
Hongbo Zhao
claimed_verified
2025-02-18T09:32:42.698Z
64100834c025ddf6189c415e
/avatars/9b9bbecef5d5815540abf92d74012f55.svg
Hongbo Zhao
user
z-hb
67b2dedc8a276e7b485a9bd0
Le Lu
67b2dedc8a276e7b485a9bd1
Ge Yang
67b2dedc8a276e7b485a9bd2
Minfeng Xu
MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE
  Solvers
In applications of diffusion models, controllable generation is of practical
significance, but is also challenging. Current methods for controllable
generation primarily focus on modifying the score function of diffusion models,
while Mean Reverting (MR) Diffusion directly modifies the structure of the
stochastic differential equation (SDE), making the incorporation of image
conditions simpler and more natural. However, current training-free fast
samplers are not directly applicable to MR Diffusion. And thus MR Diffusion
requires hundreds of NFEs (number of function evaluations) to obtain
high-quality samples. In this paper, we propose a new algorithm named MRS (MR
Sampler) to reduce the sampling NFEs of MR Diffusion. We solve the reverse-time
SDE and the probability flow ordinary differential equation (PF-ODE) associated
with MR Diffusion, and derive semi-analytical solutions. The solutions consist
of an analytical function and an integral parameterized by a neural network.
Based on this solution, we can generate high-quality samples in fewer steps.
Our approach does not require training and supports all mainstream
parameterizations, including noise prediction, data prediction and velocity
prediction. Extensive experiments demonstrate that MR Sampler maintains high
sampling quality with a speedup of 10 to 20 times across ten different image
restoration tasks. Our algorithm accelerates the sampling procedure of MR
Diffusion, making it more practical in controllable generation.
A new algorithm, MR Sampler, reduces the sampling steps and enhances the speed of Mean Reverting Diffusion while maintaining high quality across image restoration tasks.
diffusion models controllable generation score function Mean Reverting (MR) Diffusion stochastic differential equation (SDE) image conditions sampling NFEs (number of function evaluations) reverse-time SDE probability flow ordinary differential equation (PF-ODE) semi-analytical solutions neural network noise prediction data prediction velocity prediction image restoration tasks