Diffusion Sampling Correction via Approximately 10 Parameters
This addresses the computational inefficiency of diffusion models for practitioners, though it is an incremental improvement over existing solvers.
The paper tackles the slow sampling problem in Diffusion Probabilistic Models by proposing PCA-based Adaptive Search (PAS), which corrects sampling directions using only about 10 parameters, improving DDIM's FID on CIFAR10 from 15.69 to 4.37 with 10 sampling steps.
While powerful for generation, Diffusion Probabilistic Models (DPMs) face slow sampling challenges, for which various distillation-based methods have been proposed. However, they typically require significant additional training costs and model parameter storage, limiting their practicality. In this work, we propose PCA-based Adaptive Search (PAS), which optimizes existing solvers for DPMs with minimal additional costs. Specifically, we first employ PCA to obtain a few basis vectors to span the high-dimensional sampling space, which enables us to learn just a set of coordinates to correct the sampling direction; furthermore, based on the observation that the cumulative truncation error exhibits an ``S"-shape, we design an adaptive search strategy that further enhances the sampling efficiency and reduces the number of stored parameters to approximately 10. Extensive experiments demonstrate that PAS can significantly enhance existing fast solvers in a plug-and-play manner with negligible costs. E.g., on CIFAR10, PAS optimizes DDIM's FID from 15.69 to 4.37 (NFE=10) using only 12 parameters and sub-minute training on a single A100 GPU. Code is available at https://github.com/onefly123/PAS.