Summary of Dig: Scalable and Efficient Diffusion Models with Gated Linear Attention, by Lianghui Zhu et al.
DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
by Lianghui Zhu, Zilong Huang, Bencheng Liao, Jun Hao Liew, Hanshu Yan, Jiashi Feng, Xinggang Wang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Diffusion models with large-scale pre-training have achieved significant success in visual content generation, particularly exemplified by Diffusion Transformers (DiT). However, DiT models face challenges with quadratic complexity efficiency when handling long sequences. To address this, we introduce Diffusion Gated Linear Attention Transformers (DiG), a simple and efficient solution that incorporates the sub-quadratic modeling capability of Gated Linear Attention (GLA) into the 2D diffusion backbone. We propose two variants, plain and U-shape architecture, showing superior efficiency and competitive effectiveness compared to DiT and other sub-quadratic-time diffusion models. At higher resolutions, DiG demonstrates greater efficiency than these methods, with DiG-S/2 being 2.5x faster and saving 75.7% GPU memory compared to DiT-S/2 at a 1792 resolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at creating images from scratch. It’s been tried before, but it’s hard because the computer needs to look at lots of information all at once. The new idea is to use something called Gated Linear Attention, which helps the computer focus on what’s important. We call this new approach Diffusion Gated Linear Attention Transformers (DiG). We tested DiG and found that it works better and faster than other methods. This means we can create even more realistic images with less effort. |
Keywords
» Artificial intelligence » Attention » Diffusion
