One-step Diffusion Models with Bregman Density Ratio Matching
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918163934871552 |
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| author | Zhu, Yuanzhi Tsonis, Eleftherios Degeorge, Lucas Kalogeiton, Vicky |
| author_facet | Zhu, Yuanzhi Tsonis, Eleftherios Degeorge, Lucas Kalogeiton, Vicky |
| contents | Diffusion and flow models achieve high generative quality but remain computationally expensive due to slow multi-step sampling. Distillation methods accelerate them by training fast student generators, yet most existing objectives lack a unified theoretical foundation. In this work, we propose Di-Bregman, a compact framework that formulates diffusion distillation as Bregman divergence-based density-ratio matching. This convex-analytic view connects several existing objectives through a common lens. Experiments on CIFAR-10 and text-to-image generation demonstrate that Di-Bregman achieves improved one-step FID over reverse-KL distillation and maintains high visual fidelity compared to the teacher model. Our results highlight Bregman density-ratio matching as a practical and theoretically-grounded route toward efficient one-step diffusion generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-step Diffusion Models with Bregman Density Ratio Matching Zhu, Yuanzhi Tsonis, Eleftherios Degeorge, Lucas Kalogeiton, Vicky Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Diffusion and flow models achieve high generative quality but remain computationally expensive due to slow multi-step sampling. Distillation methods accelerate them by training fast student generators, yet most existing objectives lack a unified theoretical foundation. In this work, we propose Di-Bregman, a compact framework that formulates diffusion distillation as Bregman divergence-based density-ratio matching. This convex-analytic view connects several existing objectives through a common lens. Experiments on CIFAR-10 and text-to-image generation demonstrate that Di-Bregman achieves improved one-step FID over reverse-KL distillation and maintains high visual fidelity compared to the teacher model. Our results highlight Bregman density-ratio matching as a practical and theoretically-grounded route toward efficient one-step diffusion generation. |
| title | One-step Diffusion Models with Bregman Density Ratio Matching |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.16983 |