SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908547223126016 |
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| author | Zhao, Haoran Bai, Tong Huang, Lei Liang, Xiaoyu |
| author_facet | Zhao, Haoran Bai, Tong Huang, Lei Liang, Xiaoyu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion Zhao, Haoran Bai, Tong Huang, Lei Liang, Xiaoyu Machine Learning 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. |
| title | SAGE: Semantic-Aware Shared Sampling for Efficient Diffusion |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.15865 |