DREAM: Diffusion Rectification and Estimation-Adaptive Models
Authors: name: Jinxin Zhou, name: Tianyu Ding, name: Tianyi Chen, name: Jiachen Jiang, name: Ilya Zharkov, name: Zhihui Zhu, name: Luming Liang

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 3times  faster training convergence and a 10 to
20times reduction in necessary sampling steps to achieve comparable or
superior results. We hope DREAM will inspire a rethinking of diffusion model
training paradigms.