March 14, 2024, 4:46 a.m. | Lijun Yu, Jos\'e Lezama, Nitesh B. Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Vighnesh Birodkar, Agrim Gupta, Xiuye Gu, Alexand

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arXiv:2310.05737v2 Announce Type: replace
Abstract: While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation. To effectively use LLMs for visual generation, one crucial component is the visual tokenizer that maps pixel-space inputs to discrete tokens appropriate for LLM learning. In this paper, we introduce MAGVIT-v2, a video tokenizer designed to generate concise and expressive tokens for both videos and images using …

abstract arxiv diffusion diffusion models generative image key language language model language models large language large language models llms maps tasks type video video generation visual

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