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.
For k ≥ 0, DKL(pdata ∥ pk+1,γ 0 )−DKL(pdata ∥ pk,γ 0 ) ≤ −γD KL(p∗ τ ∥pk,γ τ )
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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.