Summary of Ec-dit: Scaling Diffusion Transformers with Adaptive Expert-choice Routing, by Haotian Sun et al.
EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing
by Haotian Sun, Tao Lei, Bowen Zhang, Yanghao Li, Haoshuo Huang, Ruoming Pang, Bo Dai, Nan Du
First submitted to arxiv on: 2 Oct 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 The paper introduces a new family of diffusion transformer models, called EC-DIT (Explicitly Combining Diverse Image Transformations), which leverages computational heterogeneity to efficiently scale text-to-image synthesis. By developing Mixture-of-Experts (MoE) models with expert-choice routing, EC-DIT adapts compute allocation based on input texts and image complexities, allowing for heterogeneous computation. This approach enables scaling up to 97 billion parameters and achieves significant improvements in training convergence, alignment, and generation quality compared to dense models and conventional MoE models. The paper demonstrates the effectiveness of EC-DIT through ablations, showcasing its superior scalability and adaptive compute allocation capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create images based on words. This is what a new type of computer model can do. It’s called EC-DIT, short for Explicitly Combining Diverse Image Transformations. This model is special because it uses different parts of the brain (or rather, computer) to process different types of information. It’s like having a team of experts working together to create an image based on what someone writes. The paper shows that this approach works really well and can even handle very complex tasks. In fact, it does so much better than other approaches that it sets a new standard for creating images from words. |
Keywords
» Artificial intelligence » Alignment » Diffusion » Image synthesis » Mixture of experts » Transformer
