Summary of Realcompo: Balancing Realism and Compositionality Improves Text-to-image Diffusion Models, by Xinchen Zhang et al.
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
by Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Kai-Ni Wang, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin Cui
First submitted to arxiv on: 20 Feb 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 This paper proposes a novel framework called RealCompo for text-to-image generation, which leverages the strengths of text-to-image models and spatial-aware image diffusion models to generate realistic and compositional images. The framework uses an intuitive balancer to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Experimental results show that RealCompo outperforms state-of-the-art text-to-image models and spatial-aware image diffusion models in multiple-object compositional generation while maintaining satisfactory realism and compositionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RealCompo is a new way to make pictures from words. It combines two kinds of computer programs to create more realistic and detailed images with multiple objects. The special part about RealCompo is that it can use different computer programs without needing any extra training or setup. This makes it very useful for creating images that are both realistic and well-composed. |
Keywords
* Artificial intelligence * Image generation
