Papers › Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Lijie Fan, Tianhong Li, Siyang Qin, Yuanzhen Li, Chen Sun, Michael Rubinstein, Deqing Sun, Kaiming He, 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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Color Attri. | 0.51 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Colors | 0.80 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Counting | 0.63 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Overall | 0.69 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Position | 0.39 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Single Obj. | 0.96 | #13 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | GenEval | Fluid (10.5B) | Two Obj. | 0.83 | #13 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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