Summary of Diffusion Sampling Correction Via Approximately 10 Parameters, by Guangyi Wang et al.
Diffusion Sampling Correction via Approximately 10 Parameters
by Guangyi Wang, Wei Peng, Lijiang Li, Wenyu Chen, Yuren Cai, Songzhi Su
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Diffusion Probabilistic Models (DPMs) have shown remarkable performance in generative tasks, but this comes at the expense of reduced sampling efficiency. To address this issue without compromising quality, researchers have proposed distillation-based accelerated sampling algorithms. However, these methods typically require significant additional training costs and model parameter storage, limiting their practical application. In this paper, we propose PCA-based Adaptive Search (PAS), which optimizes existing solvers for DPMs with minimal learnable parameters and training costs. By employing Principal Component Analysis (PCA) to obtain a few orthogonal unit basis vectors spanning the high-dimensional sampling space, PAS learns only a set of coordinates to correct the sampling direction. Additionally, based on the observation that the cumulative truncation error exhibits an “S”-shape, we design an adaptive search strategy that further enhances sampling efficiency and reduces 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of computer model called Diffusion Probabilistic Models work faster without sacrificing quality. These models are really good at creating new images, but they take a long time to make them. To speed them up, researchers have been trying different methods. One problem with these methods is that they require a lot of extra training and storage space. In this paper, we propose a new method called PCA-based Adaptive Search (PAS) that makes the models work faster without needing all that extra stuff. PAS uses something called Principal Component Analysis to make the model better at finding good solutions quickly. We tested it on some images and found that it really works! |
Keywords
» Artificial intelligence » Diffusion » Distillation » Pca » Principal component analysis
