arXiv:2602.18647v2 Announce Type: replace Abstract: We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels where denoising is most informative. Together with loss weighting, a noise schedule induces an effective allocation across denoising problems, often fixed before informative noise levels are known. InfoNoise makes this allocation data-adaptive by estimating a conditional-entropy-rate profile from denoising losses during training, without auxiliary models or offline search. Through I--MMSE, this profile iden
The rapid advancement and adoption of diffusion models for generative AI are driving continuous innovation in training efficiency and performance.
Improved noise scheduling directly enhances the efficiency and quality of generative AI model training, impacting resource consumption and model capabilities.
Diffusion model training can now dynamically adapt noise allocation, leading to more efficient learning and potentially better output quality without manual tuning.
- · AI developers
- · Generative AI platforms
- · Cloud computing providers
- · Research institutions
- · Less efficient generative AI methods
- · Manual hyperparameter tuners
Reduced computational costs and faster development cycles for diffusion models.
Accelerated deployment of more sophisticated and higher-quality generative AI applications across various industries.
Potentially democratized access to advanced generative AI capabilities due to lower resource barriers, fostering broader innovation.
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Read at arXiv cs.LG