DREAM: Diffusion Rectification and Estimation-Adaptive Models
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.
656d3b848fb1ddf0d553f9fb
Jinxin Zhou
admin_assigned
2023-12-04T10:50:12.159Z
653ecd3cffd60206c8c3f1a1
/avatars/866d4e65de6da31e1a3f3842db51b798.svg
Jinxin Zhou
user
jinxinz
656d3b848fb1ddf0d553f9fc
Tianyu Ding
admin_assigned
2023-12-04T10:50:18.467Z
646b0c6d3e2a7b0659452fc7
https://cdn-avatars.huggingface.co/v1/production/uploads/646b0c6d3e2a7b0659452fc7/aqT9nbrJtOk38tDw_12xW.jpeg
Tianyu Ding
user
tding1
656d3b848fb1ddf0d553f9fd
Tianyi Chen
claimed_verified
2023-12-05T08:40:54.745Z
64ad94f05a4a60156925ec96
/avatars/643bdb076e703bfcc89cec6fccb756c6.svg
Tianyi Chen
user
tianyic
656d3b848fb1ddf0d553f9fe
Jiachen Jiang
656d3b848fb1ddf0d553f9ff
Ilya Zharkov
656d3b848fb1ddf0d553fa00
Zhihui Zhu
656d3b848fb1ddf0d553fa01
Luming Liang
DREAM: Diffusion Rectification and Estimation-Adaptive Models
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.
DREAM, a novel training framework for diffusion models, improves training alignment with sampling, achieving faster convergence and reduced sampling steps in image super-resolution.
diffusion rectification estimation adaptation diffusion models image super-resolution