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The sampling process of continuous diffusion models can be realized as a deterministic trajectory via the probability flow ordinary differential equation (ODE), supporting accelerated sampling and consistency distillation.
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10/30/2026
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UnverifiedCommon inference acceleration techniques include consistency distillation, flow matching, and diffusion model step compression methods such as LCM and SDXL-Turbo.75% similarUnverifiedThe technical principle behind four-step generation typically comes from accelerated sampling paradigms such as Consistency Models or Flow Matching, which use distillation training to enable the model to accurately predict the denoising direction at sparse time steps.75% similarUnverifiedThe arXiv:2610.09045v1 study proposes using conditional diffusion models to learn the transition kernel of time-homogeneous jump-diffusion processes in a data-driven manner and to generate new sample paths75% similarUnverifiedThe theoretical foundation of diffusion models was unified by Song Yang et al. in 2020 through a stochastic differential equation (SDE) framework; the discovery of probability flow ODEs enabled deterministic sampling and gave rise to accelerated sampling methods such as DDIM, compressing the number of sampling steps from 1000 to 10–50.73% similarUnverifiedMainstream generative models such as diffusion models and flow matching models are essentially probabilistic generators that produce new samples by sampling from data distributions.73% similar
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