Summary of Mulan: Adapting Multilingual Diffusion Models For Hundreds Of Languages with Negligible Cost, by Sen Xing et al.
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible Cost
by Sen Xing, Muyan Zhong, Zeqiang Lai, Liangchen Li, Jiawen Liu, Yaohui Wang, Jifeng Dai, Wenhai Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 presents MuLan, a novel framework for multilingual image generation that leverages text encoders pre-trained on noisy Internet data. Unlike models tuned on high-quality images with multilingual annotations, MuLan achieves comparable generation capabilities in over 110 languages using readily accessible English data and off-the-shelf multilingual text encoders. The framework comprises a lightweight language adapter (Multi-Language adapter) with fewer than 20M parameters, trained alongside a frozen text encoder and image diffusion model. This cost-effective approach minimizes training costs while achieving high performance, making it suitable for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to generate images in many different languages without needing huge amounts of training data. That’s what this research is all about! The team developed a new way to create pictures that can be understood by people speaking over 110 languages. They did this by using a special kind of computer program that can learn from noisy Internet data, which is much cheaper and easier to work with than traditional methods. This breakthrough has the potential to make image generation more accessible and useful for many different purposes. |
Keywords
» Artificial intelligence » Diffusion model » Encoder » Image generation
