Fluid: Scaling Autoregressive Text-to-image Generative Models with
  Continuous Tokens
Authors: name: Lijie Fan, name: Tianhong Li, name: Siyang Qin, name: Yuanzhen Li, name: Chen Sun, name: Michael Rubinstein, name: Deqing Sun, name: Kaiming He, name: Yonglong Tian

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.