Summary of Terdit: Ternary Diffusion Models with Transformers, by Xudong Lu et al.
TerDiT: Ternary Diffusion Models with Transformers
by Xudong Lu, Aojun Zhou, Ziyi Lin, Qi Liu, Yuhui Xu, Renrui Zhang, Yafei Wen, Shuai Ren, Peng Gao, Junchi Yan, Hongsheng Li
First submitted to arxiv on: 23 May 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 proposes a novel approach to efficient deployment of large-scale pre-trained text-to-image diffusion models, specifically targeting transformer-based diffusion models (DiTs). The authors introduce TerDiT, a quantization-aware training (QAT) and deployment scheme that enables ternary diffusion models with transformers. This work focuses on scaling down the model size from 600M to 4.2B while maintaining competitive image generation capabilities compared to full-precision models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This breakthrough technology can generate high-fidelity images, particularly using transformer-based diffusion models like DiTs. The authors developed a new method called TerDiT, which helps reduce the size of these large-scale models, making them more affordable and practical for real-world applications. This research aims to improve image generation capabilities while decreasing computational costs. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Precision » Quantization » Transformer
