DREAM: Diffusion Rectification and Estimation-Adaptive Models

• URL: http://arxiv.org/abs/2312.00210v2
• Date: Tue, 19 Mar 2024 22:19:18 GMT
• Title: DREAM: Diffusion Rectification and Estimation-Adaptive Models
• Authors: Jinxin Zhou, Tianyu Ding, Tianyi Chen, Jiachen Jiang, Ilya Zharkov, Zhihui Zhu, Luming Liang,
• Abstract summary: We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling process, and estimation adaptation, which balances perception against distortion.
• Score: 50.66535824749801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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