Summary of Controllable Image Generation with Composed Parallel Token Prediction, by Jamie Stirling and Noura Al-moubayed
Controllable Image Generation With Composed Parallel Token Prediction
by Jamie Stirling, Noura Al-Moubayed
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel method for compositional image generation by combining the log-probability outputs of discrete generative models in the latent space. The approach achieves state-of-the-art generation accuracy in three distinct settings while maintaining competitive Fréchet Inception Distance (FID) scores. The model outperforms comparable continuous methods in terms of speed, with a 2.3-12x speedup on current hardware. Furthermore, the method allows for interpretable dimensionality control via concept weighting and can generalize to combinations of input conditions outside training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better pictures by combining different parts together. Right now, we’re not very good at making new images that have never been seen before during training. To fix this, the authors came up with a way to use different building blocks for making these new images. They tested it on some examples and showed that their method is really good at creating pictures that look like they should! It’s also much faster than other methods that can do similar things. |
Keywords
» Artificial intelligence » Image generation » Latent space » Probability
