Summary of On the Scalability Of Diffusion-based Text-to-image Generation, by Hao Li et al.
On the Scalability of Diffusion-based Text-to-Image Generation
by Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto
First submitted to arxiv on: 3 Apr 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 The paper explores the scaling properties of diffusion-based text-to-image (T2I) models, which have been successful for large language models but are less well understood for T2I. The authors perform extensive ablations to study how scaling both denoising backbones and training sets affects performance. They find that model scaling is influenced by cross-attention and that increasing transformer blocks is more efficient than increasing channel numbers. An efficient UNet variant is identified, which is smaller and faster than previous designs. The paper also shows that data scaling matters more than simply dataset size, with caption density and diversity improving text-image alignment performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make better text-to-image models using computers. It tries different ways of making the model bigger or changing what it’s trained on to see what works best. The authors find that making some parts of the model bigger helps, but not as much as you might think. They also find a way to make the model smaller and faster while still keeping it good at its job. Another thing they learn is that having more variety in the pictures and captions used to train the model makes it better. |
Keywords
* Artificial intelligence * Alignment * Cross attention * Diffusion * Transformer * Unet
