Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-image generation, focusing on two critical factors: whether models use discrete or continuous tokens, and whether tokens are generated in a random or fixed raster order using BERT- or GPT-like transformer architectures. Our empirical results show that, while all models scale effectively in terms of validation loss, their evaluation performance -- measured by FID, GenEval score, and visual quality -- follows different trends. Models based on continuous tokens achieve significantly better visual quality than those using discrete tokens. Furthermore, the generation order and attention mechanisms significantly affect the GenEval score: random-order models achieve notably better GenEval scores compared to raster-order models. Inspired by these findings, we train Fluid, a random-order autoregressive model on continuous tokens. Fluid 10.5B model achieves a new state-of-the-art zero-shot FID of 6.16 on MS-COCO 30K, and 0.69 overall score on the GenEval benchmark. We hope our findings and results will encourage future efforts to further bridge the scaling gap between vision and language models.

本研究解决了自回归模型在文本到图像生成中扩展性不足的问题，探讨了使用离散令牌和连续令牌的影响，以及生成令牌的顺序。结果表明，基于连续令牌的模型在视觉质量上显著优于离散令牌模型，且随机生成的模型在GenEval评分上表现更佳。Fluid模型在MS-COCO 30K上实现了新的无监督FID最优状态，推动了视觉和语言模型之间的扩展研究。

Fluid：利用连续令牌扩展自回归文本到图像生成模型