🤖 AI Summary
Diffusion probabilistic models (DPMs) suffer from slow sampling and numerical divergence under high classifier-free guidance (CFG) scales. To address this, we propose a stable and efficient high-order ODE-guided sampling framework. Our method introduces three key innovations: (1) the first high-order explicit solver specifically designed for data-prediction DPMs; (2) a threshold-based distribution correction mechanism to suppress gradient explosion at large CFG scales; and (3) a multi-step adaptive step-size reduction strategy to ensure numerical stability. Evaluated on both pixel-space and latent-space DPMs, our approach generates high-fidelity samples in only 15–20 function evaluations—over 5× faster than DDIM—while maintaining robust convergence across CFG=15–25. The implementation is publicly available.
📝 Abstract
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 DDIM, a first-order diffusion 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 large. 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.
