Summary of Truncated Consistency Models, by Sangyun Lee et al.
Truncated Consistency Models
by Sangyun Lee, Yilun Xu, Tomas Geffner, Giulia Fanti, Karsten Kreis, Arash Vahdat, Weili Nie
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces a new approach to consistency models, which accelerate sampling from diffusion models by predicting the solution of probability flow ODEs. However, training these models requires mapping intermediate points to their corresponding endpoints, making one-step generation challenging. To address this, the authors propose generalizing consistency training to truncated time ranges, allowing the model to focus on generation. They also introduce a new parameterization and two-stage training procedure to prevent trivial solutions. The method achieves better one-step and two-step FIDs than state-of-the-art consistency models like iCT-deep using smaller networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a way to make computers generate images faster by predicting what an image will look like at different stages of creation. This helps the computer focus on making the final image rather than getting stuck in earlier steps. The new method is called truncated consistency training and it uses two-stage training to prevent the computer from just guessing the same answer over and over again. The results show that this method can generate images with higher quality using less powerful computers. |
Keywords
» Artificial intelligence » Diffusion » Probability
