Summary of Golden Noise For Diffusion Models: a Learning Framework, by Zikai Zhou and Shitong Shao and Lichen Bai and Zhiqiang Xu and Bo Han and Zeke Xie
Golden Noise for Diffusion Models: A Learning Framework
by Zikai Zhou, Shitong Shao, Lichen Bai, Zhiqiang Xu, Bo Han, Zeke Xie
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 machine learning framework for obtaining “golden noises” in text-to-image diffusion models. The authors identify a new concept called the “noise prompt,” which adds a small perturbation to a random Gaussian noise to produce a golden noise aligned with a given text prompt. They develop a “noise prompt learning” framework that learns prompted golden noises and collect a large-scale dataset of paired random and golden noises. Using this dataset, they train a small network (NPNet) that can transform random noises into golden noises. The authors demonstrate the effectiveness of NPNet in improving synthesized image quality across various diffusion models with limited additional inference and computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about learning “golden noises” for text-to-image diffusion models. Golden noises are special kinds of noise that help create better images when given a text prompt. The researchers develop a new idea called the “noise prompt,” which adds some extra information to random noise to make it golden. They also collect a big dataset of pairs of random and golden noises, and train a small computer program (NPNet) to turn random noises into golden ones. This helps create better images without needing too much extra computing power. |
Keywords
» Artificial intelligence » Diffusion » Inference » Machine learning » Prompt
