Papers › Autoregressive Image Generation without Vector Quantization
Autoregressive Image Generation without Vector Quantization
Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, Kaiming He
Conventional wisdom holds that autoregressive models for image generation are typically accompanied by vector-quantized tokens. We observe that while a discrete-valued space can facilitate representing a categorical distribution, it is not a necessity for autoregressive modeling. In this work, we propose to model the per-token probability distribution using a diffusion procedure, which allows us to apply autoregressive models in a continuous-valued space. Rather than using categorical cross-entropy loss, we define a Diffusion Loss function to model the per-token probability. This approach eliminates the need for discrete-valued tokenizers. We evaluate its effectiveness across a wide range of cases, including standard autoregressive models and generalized masked autoregressive (MAR) variants. By removing vector quantization, our image generator achieves strong results while enjoying the speed advantage of sequence modeling. We hope this work will motivate the use of autoregressive generation in other continuous-valued domains and applications. Code is available at: https://github.com/LTH14/mar.
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Code
Syntology Ran 8 of 13 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · fixture could not drive it; 5 ran with no contract checked.
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Code Syntology ran Syntology
13 samples harvested; 8 ran; 1 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 1 of the 13 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from lth14/mar. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | ImageNet 256x256 | MAR-H, Diff Loss | FID | 1.55 | #24 of 94 | Archive leaderboard | report |
| Image Generation | ImageNet 256x256 | MAR-L, Diff Loss | FID | 1.78 | #39 of 94 | Archive leaderboard | report |
| Image Generation | ImageNet 256x256 | MAR-B, Diff Loss | FID | 2.31 | #56 of 94 | Archive leaderboard | report |
| Image Generation | ImageNet 512x512 | MAR-L, Diff Loss | FID | 1.73 | #17 of 52 | 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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