Summary of Draw Like An Artist: Complex Scene Generation with Diffusion Model Via Composition, Painting, and Retouching, by Minghao Liu et al.
Draw Like an Artist: Complex Scene Generation with Diffusion Model via Composition, Painting, and Retouching
by Minghao Liu, Le Zhang, Yingjie Tian, Xiaochao Qu, Luoqi Liu, Ting Liu
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 generating complex scenes using text-to-image diffusion models. The authors define complex scenes and introduce Complex Decomposition Criteria (CDC) to decompose prompts into three stages: composition, painting, and retouching. They develop a training-free framework called Complex Diffusion (CxD), which leverages large language models (LLMs) to manage composition and layout, attention modulation to guide prompt completion, and retouching to enhance image details. The method outperforms previous state-of-the-art approaches in generating high-quality, semantically consistent, and visually diverse images for complex scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to make pictures from words. It says what “complex scenes” are and makes rules (Complex Decomposition Criteria) to break them down into three parts: making the scene, painting it, and adding details. They use big language models to decide how to draw things, and special attention to help with tricky parts. Then they add more detail to make the picture look even better. The results are super good and way better than what other methods can do. |
Keywords
» Artificial intelligence » Attention » Diffusion » Prompt
