Summary of Locref-diffusion:tuning-free Layout and Appearance-guided Generation, by Fan Deng et al.
LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation
by Fan Deng, Yaguang Wu, Xinyang Yu, Xiangjun Huang, Jian Yang, Guangyu Yan, Qiang Xu
First submitted to arxiv on: 22 Nov 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 The paper presents a novel text-to-image model called LocRef-Diffusion that can generate multiple instances within an image with personalized customization. The model uses a Layout-net to control instance placement and an appearance-net to improve the fidelity of instance appearances. Experiments on COCO and OpenImages datasets show state-of-the-art performance in layout and appearance guided generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to add people, animals, or objects to a picture. This paper makes it easier by creating a special model that can place these things anywhere within an image and make them look exactly like they should. The model uses two new techniques: one helps decide where to put the objects and the other makes sure they look right. The researchers tested this model on big datasets and found that it works better than any previous model. |
Keywords
» Artificial intelligence » Diffusion
