# Forward-Value Discretization

Stochastic diffusion samplers have lagged deterministic ones, and raising approximation order does not necessarily improve their strong convergence order. F-DDPM evaluates the data-prediction model at a cleaner lookahead point built from the previous prediction and the previously injected noise, still with one model evaluation per iteration and a first-order strong convergence guarantee. On CIFAR-10, ImageNet, FFHQ and LSUN it reaches lower FID than the evaluated stochastic baselines at matched NFE budgets, and with 1.5 times the NFE it approaches or beats deterministic baselines in many settings.

Stochastic diffusion samplers have lagged deterministic ones, and raising approximation order does not necessarily improve their strong convergence order. F-DDPM evaluates the data-prediction model at a cleaner lookahead point built from the previous prediction and the previously injected noise, still with one model evaluation per iteration and a first-order strong convergence guarantee. On CIFAR-10, ImageNet, FFHQ and LSUN it reaches lower FID than the evaluated stochastic baselines at matched NFE budgets, and with 1.5 times the NFE it approaches or beats deterministic baselines in many settings.