We present Open-MAGVIT2, a family of auto-regressive image generation models ranging from 300M to 1.5B. The Open-MAGVIT2 project produces an open-source replication of Google's MAGVIT-v2 tokenizer, a tokenizer with a super-large codebook (i.e., $2^{18}$ codes), and achieves the state-of-the-art reconstruction performance (1.17 rFID) on ImageNet $256 \times 256$. Furthermore, we explore its application in plain auto-regressive models and validate scalability properties. To assist auto-regressive models in predicting with a super-large vocabulary, we factorize it into two sub-vocabulary of different sizes by asymmetric token factorization, and further introduce "next sub-token prediction" to enhance sub-token interaction for better generation quality. We release all models and codes to foster innovation and creativity in the field of auto-regressive visual generation.

本研究解决了当前自回归图像生成模型在高效生成大词汇量图像时面临的挑战。我们提出了一种新的方法，通过不对称令牌因式分解优化模型的词汇量，并引入“下一个子令牌预测”以提升生成质量。研究结果显示，Open-MAGVIT2在图像重建方面达到了最先进的性能，并促进了这一领域的创新与创造力。

Open-MAGVIT2：一个迈向民主化自回归视觉生成的开源项目