Noise Conditional Variational Score Distillation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909645934690304 |
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| author | Peng, Xinyu Zheng, Ziyang Wang, Yaoming Li, Han Kan, Nuowen Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai |
| author_facet | Peng, Xinyu Zheng, Ziyang Wang, Yaoming Li, Han Kan, Nuowen Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai |
| contents | We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by revealing that the unconditional score function implicitly characterizes the score function of denoising posterior distributions. By integrating this insight into the Variational Score Distillation (VSD) framework, we enable scalable learning of generative denoisers capable of approximating samples from the denoising posterior distribution across a wide range of noise levels. The proposed generative denoisers exhibit desirable properties that allow fast generation while preserve the benefit of iterative refinement: (1) fast one-step generation through sampling from pure Gaussian noise at high noise levels; (2) improved sample quality by scaling the test-time compute with multi-step sampling; and (3) zero-shot probabilistic inference for flexible and controllable sampling. We evaluate NCVSD through extensive experiments, including class-conditional image generation and inverse problem solving. By scaling the test-time compute, our method outperforms teacher diffusion models and is on par with consistency models of larger sizes. Additionally, with significantly fewer NFEs than diffusion-based methods, we achieve record-breaking LPIPS on inverse problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise Conditional Variational Score Distillation Peng, Xinyu Zheng, Ziyang Wang, Yaoming Li, Han Kan, Nuowen Dai, Wenrui Li, Chenglin Zou, Junni Xiong, Hongkai Computer Vision and Pattern Recognition We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by revealing that the unconditional score function implicitly characterizes the score function of denoising posterior distributions. By integrating this insight into the Variational Score Distillation (VSD) framework, we enable scalable learning of generative denoisers capable of approximating samples from the denoising posterior distribution across a wide range of noise levels. The proposed generative denoisers exhibit desirable properties that allow fast generation while preserve the benefit of iterative refinement: (1) fast one-step generation through sampling from pure Gaussian noise at high noise levels; (2) improved sample quality by scaling the test-time compute with multi-step sampling; and (3) zero-shot probabilistic inference for flexible and controllable sampling. We evaluate NCVSD through extensive experiments, including class-conditional image generation and inverse problem solving. By scaling the test-time compute, our method outperforms teacher diffusion models and is on par with consistency models of larger sizes. Additionally, with significantly fewer NFEs than diffusion-based methods, we achieve record-breaking LPIPS on inverse problems. |
| title | Noise Conditional Variational Score Distillation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.09416 |