SFBD Flow converts the iterative SFBD approach into a continuous optimization framework for diffusion models on noisy samples, with its Online SFBD instantiation outperforming baselines.
+ E D(mk 0 ) Z τ 0 1 2 ∥b(xt, t) − st(xt)∥2 dt, where bk(xt, t) is the drift of the backward SDE starting from τ with the initial distribution mk 0 ∗ N (0, τI)
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SFBD Flow: A Continuous-Optimization Framework for Training Diffusion Models with Noisy Samples
SFBD Flow converts the iterative SFBD approach into a continuous optimization framework for diffusion models on noisy samples, with its Online SFBD instantiation outperforming baselines.