Model-aware diffusion schedules that adapt to prediction difficulty outperform fixed schedules, and the optimal allocation patterns are surprisingly consistent across different models and datasets—enabling a universal template that works without per-model tuning.
This paper improves diffusion and flow-matching models by designing schedules (signal/noise mixing ratios) that account for how well the model actually predicts at different stages.