Summary of Improving Diffusion-based Image Synthesis with Context Prediction, by Ling Yang et al.
Improving Diffusion-Based Image Synthesis with Context Prediction
by Ling Yang, Jingwei Liu, Shenda Hong, Zhilong Zhang, Zhilin Huang, Zheming Cai, Wentao Zhang, Bin Cui
First submitted to arxiv on: 4 Jan 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 research paper presents a novel approach to improving the quality and diversity of image synthesis using diffusion models. Existing diffusion models focus on reconstructing input images from corrupted ones, but this may not preserve neighborhood context, leading to impaired synthesis. The authors propose ConPreDiff, which predicts context at each point using a decoder, allowing for better reconstruction by preserving semantic connections. This method generalizes to arbitrary backbones without adding parameters and outperforms previous methods in unconditional image generation, text-to-image generation, and image inpainting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ConPreDiff is a new way to make images using computer algorithms. Current methods try to fix broken pictures, but they don’t always get the details right. The authors of this paper thought about how context can help, like what’s in the picture or where things are. They developed ConPreDiff, which predicts what’s around each part of the image and uses that information to make a better picture. This method works with different kinds of algorithms and does better than previous methods at making pictures from scratch, fixing broken pictures, and putting words into images. |
Keywords
* Artificial intelligence * Decoder * Image generation * Image inpainting * Image synthesis
