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Gradient Guidance for Diffusion Models: An Optimization Perspective
April 24, 2024, 4:42 a.m. | Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: Diffusion models have demonstrated empirical successes in various applications and can be adapted to task-specific needs via guidance. This paper introduces a form of gradient guidance for adapting or fine-tuning diffusion models towards user-specified optimization objectives. We study the theoretic aspects of a guided score-based sampling process, linking the gradient-guided diffusion model to first-order optimization. We show that adding gradient guidance to the sampling process of a pre-trained diffusion model is essentially equivalent to solving …
abstract applications arxiv cs.lg diffusion diffusion models fine-tuning form gradient guidance optimization paper perspective process sampling stat.ml study type via
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