title: Fluid: Scaling Autoregressive Text-to-image Generative Models with
    Continuous Tokens
summary: 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.
paper_authors._id: 6712be0667f2290f5689565b
paper_authors.name: Lijie Fan
paper_authors._id: 6712be0667f2290f5689565c
paper_authors.name: Tianhong Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T22:09:39.040Z
paper_authors._id: 65244352e0823fbfef8c6475
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65244352e0823fbfef8c6475/d6YipDIYHdhP_B6RJACNc.jpeg
paper_authors.fullname: Tianhong Li
paper_authors.type: user
paper_authors.user: LTH14
paper_authors._id: 6712be0667f2290f5689565d
paper_authors.name: Siyang Qin
paper_authors._id: 6712be0667f2290f5689565e
paper_authors.name: Yuanzhen Li
paper_authors._id: 6712be0667f2290f5689565f
paper_authors.name: Chen Sun
paper_authors._id: 6712be0667f2290f56895660
paper_authors.name: Michael Rubinstein
paper_authors._id: 6712be0667f2290f56895661
paper_authors.name: Deqing Sun
paper_authors._id: 6712be0667f2290f56895662
paper_authors.name: Kaiming He
paper_authors._id: 6712be0667f2290f56895663
paper_authors.name: Yonglong Tian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-21T07:48:07.444Z
paper_authors._id: 64793086ac77bb7603842456
paper_authors.avatarUrl: /avatars/5891292138d372a9cc063f86c08b8174.svg
paper_authors.fullname: Yonglong Tian
paper_authors.type: user
paper_authors.user: tyl5566
paper_title: Fluid: Scaling Autoregressive Text-to-image Generative Models with
    Continuous Tokens
paper_summary: 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.
paper_ai_keywords: autoregressive models text-to-image generation discrete tokens continuous tokens random-order raster-order BERT-like transformer architectures GPT-like transformer architectures validation loss FID GenEval score visual quality attention mechanisms Fluid Fluid 10.5B MS-COCO 30K GenEval benchmark zero-shot FID