Fluid: Scaling Autoregressive Text-to-image Generative Models with
  Continuous Tokens
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.
6712be0667f2290f5689565b
Lijie Fan
6712be0667f2290f5689565c
Tianhong Li
claimed_verified
2025-02-27T22:09:39.040Z
65244352e0823fbfef8c6475
https://cdn-avatars.huggingface.co/v1/production/uploads/65244352e0823fbfef8c6475/d6YipDIYHdhP_B6RJACNc.jpeg
Tianhong Li
user
LTH14
6712be0667f2290f5689565d
Siyang Qin
6712be0667f2290f5689565e
Yuanzhen Li
6712be0667f2290f5689565f
Chen Sun
6712be0667f2290f56895660
Michael Rubinstein
6712be0667f2290f56895661
Deqing Sun
6712be0667f2290f56895662
Kaiming He
6712be0667f2290f56895663
Yonglong Tian
claimed_verified
2024-10-21T07:48:07.444Z
64793086ac77bb7603842456
/avatars/5891292138d372a9cc063f86c08b8174.svg
Yonglong Tian
user
tyl5566
Fluid: Scaling Autoregressive Text-to-image Generative Models with
  Continuous Tokens
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.
Autoregressive models for text-to-image generation achieve better visual quality with continuous tokens and random generation order compared to discrete tokens and raster order.
autoregressive models text-to-image generation discrete tokens continuous tokens random-order raster-order BERT GPT transformer architectures validation loss FID GenEval Fluid zero-shot MS-COCO 30K GenEval benchmark