Summary of Improving the Training Of Rectified Flows, by Sangyun Lee et al.
Improving the Training of Rectified Flows
by Sangyun Lee, Zinan Lin, Giulia Fanti
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 In this paper, researchers aim to improve the efficiency of diffusion models for image and video generation. They focus on rectified flows, a technique that iteratively learns smooth ODE paths to reduce truncation error. The authors propose new methods for training these flows, enabling them to compete with knowledge distillation methods in low computational settings. Specifically, they show that using a single iteration of the Reflow algorithm is sufficient to learn nearly straight trajectories, eliminating the need for multiple iterations. By introducing a U-shaped timestep distribution and LPIPS-Huber premetric, they improve one-round training of rectified flows. This leads to significant improvements in FID scores on CIFAR-10 (up to 75% better) and ImageNet 64×64 (outperforming state-of-the-art distillation methods). The proposed techniques are available at the provided GitHub link. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a type of computer program more efficient. Computer programs called diffusion models can create realistic images and videos, but they require a lot of computing power to do so. The researchers in this paper want to make these programs faster by improving a technique called rectified flows. They found that by using the right settings, they only need to run the program once instead of multiple times, which makes it much faster. This improvement works well on two different types of images: simple pictures and more complex scenes from the internet. The code for this improved program is available online. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Knowledge distillation
