Summary of Edt: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching, By Xinwang Chen and Ning Liu and Yichen Zhu and Feifei Feng and Jian Tang
EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching
by Xinwang Chen, Ning Liu, Yichen Zhu, Feifei Feng, Jian Tang
First submitted to arxiv on: 31 Oct 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 This paper proposes the Efficient Diffusion Transformer (EDT) framework to reduce the computational requirements of transformer-based Diffusion Probabilistic Models (DPMs). The EDT framework includes a lightweight-design diffusion model architecture and an attention modulation matrix inspired by human-like sketching. Additionally, the authors introduce a token relation-enhanced masking training strategy tailored for EDT. Experimental results show that EDT reduces training and inference costs while surpassing existing transformer-based DPMs in image synthesis performance. Key benefits include significant speed-ups in both training (up to 3.93x) and inference (up to 2.29x). The proposed framework is designed to enhance the efficiency of transformer-based DPMs, making them more practical for widespread applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new kind of computer model that can create realistic images faster than before. This model uses something called “transformers” and “diffusion” to make pictures look more like real life. The researchers want to make this model work better and use less computer power. They came up with a new way to build the model, using some ideas from how humans draw. They also tried a new way of training the model to help it learn better. The results show that their new method is faster and makes better pictures than before. This could be important for making computers create more realistic images in the future. |
Keywords
» Artificial intelligence » Attention » Diffusion » Diffusion model » Image synthesis » Inference » Token » Transformer
