Summary of Simplifying, Stabilizing and Scaling Continuous-time Consistency Models, by Cheng Lu et al.
Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
by Cheng Lu, Yang Song
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 presents a simplified theoretical framework that unifies previous parameterizations of diffusion models and consistency models (CMs). The authors identify the root causes of instability in continuous-time formulations, enabling them to train CMs at an unprecedented scale. They introduce key improvements in diffusion process parameterization, network architecture, and training objectives, achieving FID scores of 2.06 on CIFAR-10, 1.48 on ImageNet 64×64, and 1.88 on ImageNet 512×512 with only two sampling steps. This work narrows the gap in FID scores with the best existing diffusion models to within 10%. The proposed training algorithm uses a simplified theoretical framework, optimized for fast sampling, and is applicable to various applications such as image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve a type of artificial intelligence called consistency models. These models are used for generating images quickly and accurately. The problem was that previous versions were prone to errors because they were based on small steps. The researchers created a new framework that fixes this issue, allowing them to train the models much bigger than before. They achieved great results with their new approach, producing high-quality images with just two steps. This breakthrough could lead to more realistic and detailed image generation in various applications. |
Keywords
» Artificial intelligence » Diffusion » Image generation
