Diffusion probabilistic models(DPMs)have achieved impressive success in high-resolution image synthesis,especially in recent large-scale text-to-image generation applications.An essential technique for improving the s...Diffusion probabilistic models(DPMs)have achieved impressive success in high-resolution image synthesis,especially in recent large-scale text-to-image generation applications.An essential technique for improving the sample quality of DPMs is guided sampling,which usually needs a large guidance scale to obtain the best sample quality.The commonly-used fast sampler for guided sampling is denoising diffusion implicit models(DDIM),a first-order diffusion ordinary differential equation(ODE)solver that generally needs 100 to 250 steps for high-quality samples.Although recent works propose dedicated high-order solvers and achieve a further speedup for sampling without guidance,their effectiveness for guided sampling has not been well-tested before.In this work,we demonstrate that previous high-order fast samplers suffer from instability issues,and they even become slower than DDIM when the guidance scale grows larger.To further speed up guided sampling,we propose DPM-Solver++,a high-order solver for the guided sampling of DPMs.DPM-Solver++solves the diffusion ODE with the data prediction model and adopts thresholding methods to keep the solution matches training data distribution.We further propose a multistep variant of DPM-Solver++to address the instability issue by reducing the effective step size.Experiments show that DPM-Solver++can generate high-quality samples within only 15 to 20 steps for guided sampling by pixel-space and latent-space DPMs.展开更多
文摘Diffusion probabilistic models(DPMs)have achieved impressive success in high-resolution image synthesis,especially in recent large-scale text-to-image generation applications.An essential technique for improving the sample quality of DPMs is guided sampling,which usually needs a large guidance scale to obtain the best sample quality.The commonly-used fast sampler for guided sampling is denoising diffusion implicit models(DDIM),a first-order diffusion ordinary differential equation(ODE)solver that generally needs 100 to 250 steps for high-quality samples.Although recent works propose dedicated high-order solvers and achieve a further speedup for sampling without guidance,their effectiveness for guided sampling has not been well-tested before.In this work,we demonstrate that previous high-order fast samplers suffer from instability issues,and they even become slower than DDIM when the guidance scale grows larger.To further speed up guided sampling,we propose DPM-Solver++,a high-order solver for the guided sampling of DPMs.DPM-Solver++solves the diffusion ODE with the data prediction model and adopts thresholding methods to keep the solution matches training data distribution.We further propose a multistep variant of DPM-Solver++to address the instability issue by reducing the effective step size.Experiments show that DPM-Solver++can generate high-quality samples within only 15 to 20 steps for guided sampling by pixel-space and latent-space DPMs.
