First-Order Diffusion Samplers Can Be Fast
Research Paper#Diffusion Models, AI, Image Generation🔬 Research|Analyzed: Jan 3, 2026 06:21•
Published: Dec 31, 2025 15:35
•1 min read
•ArXivAnalysis
This paper challenges the common assumption that higher-order ODE solvers are inherently faster for diffusion probabilistic model (DPM) sampling. It argues that the placement of DPM evaluations, even with first-order methods, can significantly impact sampling accuracy, especially with a low number of neural function evaluations (NFE). The proposed training-free, first-order sampler achieves competitive or superior performance compared to higher-order samplers on standard image generation benchmarks, suggesting a new design angle for accelerating diffusion sampling.
Key Takeaways
- •Challenges the dominance of higher-order ODE solvers for DPM sampling speed.
- •Proposes a novel, training-free, first-order sampler.
- •Demonstrates competitive or superior performance compared to higher-order samplers on image generation benchmarks.
- •Highlights the importance of DPM evaluation placement for sampling accuracy.
Reference / Citation
View Original"The proposed sampler consistently improves sample quality under the same NFE budget and can be competitive with, and sometimes outperform, state-of-the-art higher-order samplers."