SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer

• URL: http://arxiv.org/abs/2412.10958v2
• Date: Fri, 20 Dec 2024 16:59:40 GMT
• Title: SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
• Authors: Hao Chen, Ze Wang, Xiang Li, Ximeng Sun, Fangyi Chen, Jiang Liu, Jindong Wang, Bhiksha Raj, Zicheng Liu, Emad Barsoum,
• Abstract summary: SoftVQ-VAE is a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token.<n>Our approach compresses 256x256 and 512x512 images using as few as 32 or 64 1-dimensional tokens.<n>Remarkably, SoftVQ-VAE improves inference throughput by up to 18x for generating 256x256 images and 55x for 512x512 images.
• Score: 45.720721058671856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient image tokenization with high compression ratios remains a critical challenge for training generative models. We present SoftVQ-VAE, a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token, substantially increasing the representation capacity of the latent space. When applied to Transformer-based architectures, our approach compresses 256x256 and 512x512 images using as few as 32 or 64 1-dimensional tokens. Not only does SoftVQ-VAE show consistent and high-quality reconstruction, more importantly, it also achieves state-of-the-art and significantly faster image generation results across different denoising-based generative models. Remarkably, SoftVQ-VAE improves inference throughput by up to 18x for generating 256x256 images and 55x for 512x512 images while achieving competitive FID scores of 1.78 and 2.21 for SiT-XL. It also improves the training efficiency of the generative models by reducing the number of training iterations by 2.3x while maintaining comparable performance. With its fully-differentiable design and semantic-rich latent space, our experiment demonstrates that SoftVQ-VAE achieves efficient tokenization without compromising generation quality, paving the way for more efficient generative models. Code and model are released.

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