Summary of Softvq-vae: Efficient 1-dimensional Continuous Tokenizer, by Hao Chen et al.
SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
by Hao Chen, Ze Wang, Xiang Li, Ximeng Sun, Fangyi Chen, Jiang Liu, Jindong Wang, Bhiksha Raj, Zicheng Liu, Emad Barsoum
First submitted to arxiv on: 14 Dec 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 A novel approach to efficient image tokenization is presented in this paper, which leverages soft categorical posteriors to increase the representation capacity of the latent space. The proposed SoftVQ-VAE method achieves high compression ratios, compressing 256×256 and 512×512 images using as few as 32 or 64 1-dimensional tokens. This leads to improved reconstruction quality and faster image generation results across different denoising-based generative models. Additionally, SoftVQ-VAE improves inference throughput by up to 18x for generating 256×256 images and 55x for 512×512 images while maintaining competitive FID scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to compress images into smaller tokens that can be used in generative models. The method, called SoftVQ-VAE, works well with different types of generative models and allows them to generate images faster and more efficiently. This is important because it could help make generative models more useful for real-world applications. |
Keywords
» Artificial intelligence » Image generation » Inference » Latent space » Tokenization
