Summary of Optimizing Few-step Sampler For Diffusion Probabilistic Model, by Jen-yuan Huang
Optimizing Few-Step Sampler for Diffusion Probabilistic Model
by Jen-Yuan Huang
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 exceptional image generation capabilities, but their practical application is hindered by the high computational cost during inference. The DPM generation process involves solving a Probability-Flow Ordinary Differential Equation (PF-ODE), which requires discretizing the integration domain into intervals for numerical approximation. Building on theoretical results, we propose a two-phase alternating optimization algorithm to optimize the sampling schedule of the PF-ODE solver and further tune the pre-trained DPM. The method consistently improves the baseline across various numbers of sampling steps, as demonstrated by experiments on the ImageNet64 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on a way to make computers generate better images using something called Diffusion Probabilistic Models (DPMs). These models can create very realistic and diverse images. The problem is that it takes a lot of computer power to do this, which makes it hard to use them in real-life situations. Scientists have found a way to solve this problem by optimizing the way the computers generate the images. They did some experiments on a big dataset called ImageNet64 and showed that their method works well. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference » Optimization » Probability
