aiqbench analysis
This research proposes using statistical sparsity as a regularization technique to significantly lower the computational costs of diffusion models without sacrificing sample quality. By mathematically reducing complexity from input dimensions to intrinsic data dimensions, this approach offers a viable pathway for scaling generative AI pipelines more efficiently. The findings provide both theoretical guarantees and empirical evidence that sparse induction can optimize inference speed in key architectures like Stable Diffusion.
Regularization can make diffusion models more efficient
arXiv:2502.09151v3 Announce Type: replace Abstract: Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates that the concept of sparsity, well known especially in statistics, can provide a pathway to more efficient diffusion pipelines. Our mathematical guarantees prove that sparsity can reduce the input dimension's influence on the computational complexity to that of a much smaller intrinsic dimension of the data. Our empirical findings confirm that inducing sparsity can indeed lead to better samples at a lower cost.
Abstract:Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates that the concept of sparsity, well known especially in statistics, can provide a pathway to more efficient diffusion pipelines. Our mathematical guarantees prove that sparsity can reduce the input dimension's influence on the computational complexity to that of a much smaller intrinsic dimension of the data. Our empirical findings confirm that inducing sparsity can indeed lead to better samples at a lower cost.
Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
arXiv:2502.09151 [cs.LG]
(or arXiv:2502.09151v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2502.09151 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 183 words
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