Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.11142 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910351997534208 |
|---|---|
| author | Kou, Siqi Gan, Lei Wang, Dequan Li, Chongxuan Deng, Zhijie |
| author_facet | Kou, Siqi Gan, Lei Wang, Dequan Li, Chongxuan Deng, Zhijie |
| contents | Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper sample-wise metric. To address this, we propose BayesDiff, a pixel-wise uncertainty estimator for generations from diffusion models based on Bayesian inference. In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference. The estimated pixel-wise uncertainty can not only be aggregated into a sample-wise metric to filter out low-fidelity images but also aids in augmenting successful generations and rectifying artifacts in failed generations in text-to-image tasks. Extensive experiments demonstrate the efficacy of BayesDiff and its promise for practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11142 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Kou, Siqi Gan, Lei Wang, Dequan Li, Chongxuan Deng, Zhijie Computer Vision and Pattern Recognition Machine Learning Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper sample-wise metric. To address this, we propose BayesDiff, a pixel-wise uncertainty estimator for generations from diffusion models based on Bayesian inference. In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference. The estimated pixel-wise uncertainty can not only be aggregated into a sample-wise metric to filter out low-fidelity images but also aids in augmenting successful generations and rectifying artifacts in failed generations in text-to-image tasks. Extensive experiments demonstrate the efficacy of BayesDiff and its promise for practical applications. |
| title | BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2310.11142 |