NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training

TL;DR

This paper introduces NoiseRater, a meta-learning framework that assigns importance scores to individual noise samples during diffusion model training, improving efficiency and quality.

Contribution

It proposes a novel parametric noise rater trained via bilevel optimization to adaptively reweight noise samples, enhancing diffusion training.

Findings

Prioritizing informative noise improves training efficiency.

Adaptive noise reweighting enhances generation quality.

Noise valuation is a new axis for diffusion model improvement.

Abstract

Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise as uniformly informative. In this work, we challenge this assumption and introduce NoiseRater, a meta-learning framework for instance-level noise valuation in diffusion model training. We propose a parametric noise rater that assigns importance scores to individual noise realizations conditioned on data and timestep, enabling adaptive reweighting of the training objective. The rater is trained via bilevel optimization to improve downstream validation performance after inner-loop diffusion updates. To enable efficient deployment, we further design a decoupled two-stage pipeline that transitions from soft weighting during meta-training to hard noise selection during standard training. Extensive experiments on FFHQ and ImageNet demonstrate that not all noise samples contribute equally, and that prioritizing informative noise improves both training efficiency and generation quality. Our results establish noise valuation as a complementary and previously underexplored axis for improving diffusion model training. Our code is available at: https://anonymous.4open.science/r/NoiseRater-DEB116.