Itô-Taylor Sampling Scheme for Denoising Diffusion Probabilistic Models using Ideal Derivatives

By Hideyuki Tachibana, Mocho Go, Muneyoshi Inahara, Yotaro Katayama, Yotaro Watanabe. 2021

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qinsheng Zhang
2. Yongxin Chen
3. Axel Sauer
4. Ling Yang
5. Xinyin Ma
6. Ye Yuan

Citations in the corpus

• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps (2022, methodology): “3.4 for using DPM-Solver in discrete-time DPMs, and then compare DPM-Solver with other discrete-time training-free samplers, including DDPM [2], DDIM [19], Analytic-DDPM [21], Analytic-DDIM [21], PNDM [22], FastDPM [38] and Itô-Taylor [24].”
• DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models (2022, unknown): “…methods for DPMs can be understood as either discretizing diffusion SDEs (Ho et al., 2020; Song et al., 2021b; Jolicoeur-Martineau et al., 2021; Tachibana et al., 2021; Kong & Ping, 2021; Bao et al., 2022b; Zhang et al., 2022) or discretizing diffusion ODEs (Song et al., 2021b;a; Liu et al.,…”
• Fast Sampling of Diffusion Models with Exponential Integrator (2022, methodology): “Another category of methods speed up the numerical solver for stochastic differential equations (SDEs) or ordinary differential equations (ODEs) associated with the DMs [14, 30, 33].”