Summary of Scalable High-resolution Pixel-space Image Synthesis with Hourglass Diffusion Transformers, by Katherine Crowson and Stefan Andreas Baumann and Alex Birch and Tanishq Mathew Abraham and Daniel Z. Kaplan and Enrico Shippole
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
by Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z. Kaplan, Enrico Shippole
First submitted to arxiv on: 21 Jan 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 HDiT is an image generative model that can be trained at high resolutions, such as 1024×1024 pixels, without requiring complex techniques like multiscale architectures or latent autoencoders. It combines the efficiency of convolutional U-Nets with the scalability of Transformer architecture. HDiT outperforms existing models on ImageNet and sets a new state-of-the-art for diffusion models on FFHQ. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HDiT is a computer program that can create realistic images. It’s special because it can work at very high resolutions, which makes it useful for tasks like generating pictures of people or animals. The model uses a combination of techniques to make it efficient and powerful. HDiT is better than other similar models at creating images. |
Keywords
* Artificial intelligence * Generative model * Transformer
