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Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible.
On the theory of stochastic processes, with particular reference to applications
William Feller · 1949
Earlier work this paper cites.
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Brian DO Anderson · 1982
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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