Summary of Towards Faster Training Of Diffusion Models: An Inspiration Of a Consistency Phenomenon, by Tianshuo Xu et al.
Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
by Tianshuo Xu, Peng Mi, Ruilin Wang, Yingcong Chen
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper explores the properties of diffusion models (DMs), a type of generative framework, to accelerate their training. The authors observe that DMs with different initializations or architectures produce similar outputs given the same noise inputs, which is rare in other generative models. They attribute this phenomenon to the learning difficulty of DMs being lower when approaching the upper bound of the timestep and the loss landscape being highly smooth, leading to similar behavior patterns. The authors propose two strategies to accelerate training: a curriculum learning based timestep schedule and a momentum decay strategy. These methods reduce training time and improve image quality by leveraging the noise rate as an indicator of learning difficulty and adjusting momentum coefficients during optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers generate images better. It looks at how some computer models, called diffusion models, can produce similar results even if they’re set up differently. The researchers found that these models are good at creating images because they learn to make them easier when the input becomes noisy. They also discovered that these models have a smooth learning process, which makes them converge to similar outcomes. To make these models train faster and better, the authors suggest two new methods: one uses a schedule to reduce training difficulty as the model improves, and the other adjusts how much momentum is used during optimization. |
Keywords
» Artificial intelligence » Curriculum learning » Diffusion » Optimization
