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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.16939 |
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| _version_ | 1866916913636966400 |
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| author | Jiang, Lei Ge, Wen Cariou-Kotlarek, Niels Yi, Mingxuan Chen, Po-Yu Yang, Lingyi Buet-Golfouse, Francois Mittal, Gaurav Ni, Hao |
| author_facet | Jiang, Lei Ge, Wen Cariou-Kotlarek, Niels Yi, Mingxuan Chen, Po-Yu Yang, Lingyi Buet-Golfouse, Francois Mittal, Gaurav Ni, Hao |
| contents | Diffusion models have achieved state-of-the-art results in generative modelling but remain computationally intensive at inference time, often requiring thousands of discretization steps. To this end, we propose Sig-DEG (Signature-based Differential Equation Generator), a novel generator for distilling pre-trained diffusion models, which can universally approximate the backward diffusion process at a coarse temporal resolution. Inspired by high-order approximations of stochastic differential equations (SDEs), Sig-DEG leverages partial signatures to efficiently summarize Brownian motion over sub-intervals and adopts a recurrent structure to enable accurate global approximation of the SDE solution. Distillation is formulated as a supervised learning task, where Sig-DEG is trained to match the outputs of a fine-resolution diffusion model on a coarse time grid. During inference, Sig-DEG enables fast generation, as the partial signature terms can be simulated exactly without requiring fine-grained Brownian paths. Experiments demonstrate that Sig-DEG achieves competitive generation quality while reducing the number of inference steps by an order of magnitude. Our results highlight the effectiveness of signature-based approximations for efficient generative modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter Jiang, Lei Ge, Wen Cariou-Kotlarek, Niels Yi, Mingxuan Chen, Po-Yu Yang, Lingyi Buet-Golfouse, Francois Mittal, Gaurav Ni, Hao Machine Learning Probability Diffusion models have achieved state-of-the-art results in generative modelling but remain computationally intensive at inference time, often requiring thousands of discretization steps. To this end, we propose Sig-DEG (Signature-based Differential Equation Generator), a novel generator for distilling pre-trained diffusion models, which can universally approximate the backward diffusion process at a coarse temporal resolution. Inspired by high-order approximations of stochastic differential equations (SDEs), Sig-DEG leverages partial signatures to efficiently summarize Brownian motion over sub-intervals and adopts a recurrent structure to enable accurate global approximation of the SDE solution. Distillation is formulated as a supervised learning task, where Sig-DEG is trained to match the outputs of a fine-resolution diffusion model on a coarse time grid. During inference, Sig-DEG enables fast generation, as the partial signature terms can be simulated exactly without requiring fine-grained Brownian paths. Experiments demonstrate that Sig-DEG achieves competitive generation quality while reducing the number of inference steps by an order of magnitude. Our results highlight the effectiveness of signature-based approximations for efficient generative modeling. |
| title | Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2508.16939 |