  We present DREAM, a novel training framework representing Diffusion
Rectification and Estimation Adaptive Models, requiring minimal code changes
(just three lines) yet significantly enhancing the alignment of training with
sampling in diffusion models. DREAM features two components: diffusion
rectification, which adjusts training to reflect the sampling process, and
estimation adaptation, which balances perception against distortion. When
applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff
between minimizing distortion and preserving high image quality. Experiments
demonstrate DREAM's superiority over standard diffusion-based SR methods,
showing a $2$ to $3\times $ faster training convergence and a $10$ to
$20\times$ reduction in sampling steps to achieve comparable results. We hope
DREAM will inspire a rethinking of diffusion model training paradigms.
