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arXiv · 2509.15865

SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion

Haoran Zhao·Tong Bai·Lei Huang·Xiaoyu Liang
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Abstract

Diffusion models manifest evident benefits across diverse domains, yet their high sampling cost, requiring dozens of sequential model evaluations, remains a major limitation. Prior efforts mainly accelerate sampling via optimized solvers or distillation, which treat each query independently. In contrast, we reduce total number of steps by sharing early-stage sampling across semantically similar queries. To enable such efficiency gains without sacrificing quality, we propose SAGE, a semantic-aware shared sampling framework that integrates a shared sampling scheme for efficiency and a tailored training strategy for quality preservation. Extensive experiments show that SAGE reduces sampling cost by 25.5%, while improving generation quality with 5.0% lower FID, 5.4% higher CLIP, and 160% higher diversity over baselines.

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Haoran Zhao, Tong Bai, Lei Huang, Xiaoyu Liang. 2025-09-19. SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion. https://arxiv.org/abs/2509.15865

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