Summary of Instancediffusion: Instance-level Control For Image Generation, by Xudong Wang et al.
InstanceDiffusion: Instance-level Control for Image Generation
by Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, Ishan Misra
First submitted to arxiv on: 5 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 InstanceDiffusion, a novel approach to text-to-image diffusion models that allows for precise instance-level control. The model supports free-form language conditions per instance and enables users to specify instance locations using various methods, including single points, scribbles, bounding boxes, and intricate masks. To achieve this control, the authors introduce three key components: UniFusion, ScaleU, and Multi-instance Sampler. These innovations enable InstanceDiffusion to significantly outperform state-of-the-art models on benchmark datasets like COCO, with a 20.4% improvement in AP50 box inputs and a 25.4% increase in IoU for mask inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better pictures from words! It’s called InstanceDiffusion, and it lets you control what’s in the picture down to the individual object. Imagine telling an AI to make a picture of a cat with its tail raised high or a dog playing fetch. That’s what this technology can do. The scientists who made it came up with three new tricks to make it work: UniFusion, ScaleU, and Multi-instance Sampler. These ideas are so good that they beat all the other AI models at making pictures from words. |
Keywords
* Artificial intelligence * Diffusion * Mask
