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Stochstic Sampling for Generative Diffusion Models: From Euler-Maruyama to Higher-Order Schemes
We develop a convergence analysis for generative diffusion models that simultaneously accounts for the three principal error sources in stochastic sampling: initialization, score matching, and discretization of the reverse-time stochastic differential equation (SDE). Our central tool is the notion o
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