Summary of Open-magvit2: An Open-source Project Toward Democratizing Auto-regressive Visual Generation, by Zhuoyan Luo et al.
Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation
by Zhuoyan Luo, Fengyuan Shi, Yixiao Ge, Yujiu Yang, Limin Wang, Ying Shan
First submitted to arxiv on: 6 Sep 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 presents an open-source implementation of Google’s MAGVIT-v2 tokenizer, a sophisticated tool that achieves state-of-the-art reconstruction performance on ImageNet and UCF benchmarks. The proposed tokenizer features a super-large codebook with 2^18 codes and outperforms Cosmos in zero-shot benchmarking (1.93 vs. 0.78 rFID on ImageNet original resolution). Additionally, the authors explore the application of this tokenizer in plain auto-regressive models to validate scalability properties, developing a family of image generation models ranging from 300M to 1.5B parameters. To further enhance model capabilities, the authors introduce asymmetric token factorization and “next sub-token prediction” for better generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a computer program that can understand and generate images really well. The program uses a special tool called a tokenizer that helps it do this. The people who made the program got it to work really well on big datasets of images, beating other programs that tried to do the same thing. They also showed how their program could be used to make even better image generation models in the future. |
Keywords
» Artificial intelligence » Image generation » Token » Tokenizer » Zero shot
