Summary of Knobgen: Controlling the Sophistication Of Artwork in Sketch-based Diffusion Models, by Pouyan Navard et al.
KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models
by Pouyan Navard, Amin Karimi Monsefi, Mengxi Zhou, Wei-Lun Chao, Alper Yilmaz, Rajiv Ramnath
First submitted to arxiv on: 2 Oct 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 This paper proposes KnobGen, a novel dual-pathway framework for text-to-image generation that balances fine-grained precision with high-level control. The authors aim to democratize sketch-based image generation by adapting to varying levels of sketch complexity and user skill. KnobGen consists of a Coarse-Grained Controller (CGC) module for high-level semantics and a Fine-Grained Controller (FGC) module for detailed refinement, which can be adjusted through the knob inference mechanism to suit specific needs. The framework is evaluated on the MultiGen-20M dataset and a newly collected sketch dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KnobGen is a new way to make pictures from text or sketches. It’s like having two special controls that work together to get the perfect picture. One control helps with big ideas, while the other one makes sure small details are just right. This means that even people who aren’t very good at drawing can use KnobGen to create beautiful images. The authors tested it on some big datasets and showed that it works really well. |
Keywords
» Artificial intelligence » Image generation » Inference » Precision » Semantics
