Summary of Mole: Enhancing Human-centric Text-to-image Diffusion Via Mixture Of Low-rank Experts, by Jie Zhu et al.
MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts
by Jie Zhu, Yixiong Chen, Mingyu Ding, Ping Luo, Leye Wang, Jingdong Wang
First submitted to arxiv on: 30 Oct 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 The paper proposes a novel approach to human-centric text-to-image generation, particularly for faces and hands. The issue with current methods is that they often lack naturalness in their outputs due to insufficient training priors. To address this, the authors collect a large dataset of high-quality images featuring humans, which serves as a prior knowledge base for enhancing image generation capabilities. Additionally, they introduce the Mixture of Low-rank Experts (MoLE) method, which uses low-rank modules trained on close-up hand and face images to refine image parts. The authors compare MoLE with state-of-the-art methods using two benchmarks and various evaluation metrics, including human studies. The results show that MoLE outperforms other methods in terms of naturalness and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make text-to-image generation better by giving it a more realistic look, especially when it comes to faces and hands. It does this by collecting lots of high-quality images with people in them and using special “experts” that are trained on close-up pictures of faces and hands. This makes the generated images look more natural and detailed. The authors test their new method against others and show that it works better. |
Keywords
» Artificial intelligence » Image generation » Knowledge base
