Papers › AliTok: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model

AliTok: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive Model

5 Jun 2025arXiv 2025 6arXiv:2506.05289archive 2025-07-28

Pingyu Wu, Kai Zhu, Yu Liu, Longxiang Tang, Jian Yang, Yansong Peng, Wei Zhai, Yang Cao, Zheng-Jun Zha

Autoregressive image generation aims to predict the next token based on previous ones. However, existing image tokenizers encode tokens with bidirectional dependencies during the compression process, which hinders the effective modeling by autoregressive models. In this paper, we propose a novel Aligned Tokenizer (AliTok), which utilizes a causal decoder to establish unidirectional dependencies among encoded tokens, thereby aligning the token modeling approach between the tokenizer and autoregressive model. Furthermore, by incorporating prefix tokens and employing two-stage tokenizer training to enhance reconstruction consistency, AliTok achieves great reconstruction performance while being generation-friendly. On ImageNet-256 benchmark, using a standard decoder-only autoregressive model as the generator with only 177M parameters, AliTok achieves a gFID score of 1.50 and an IS of 305.9. When the parameter count is increased to 662M, AliTok achieves a gFID score of 1.35, surpassing the state-of-the-art diffusion method with 10x faster sampling speed. The code and weights are available at https://github.com/ali-vilab/alitok.

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Attention ali-vilab/alitok/modeling/alitok.py official repository ran no licence file found · pointer only · 90986cade2362a6c · report
ResidualAttentionBlock ali-vilab/alitok/modeling/alitok.py official repository ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only · 71a046a9abe78001 · report
VectorQuantizer ali-vilab/alitok/modeling/alitok.py official repository ran · metamorphic tier: deterministic no licence file found · pointer only · 8c5d87429839addc · report
create_npz_from_sample_folder ali-vilab/alitok/sample_imagenet.py official repository ran · our draft was wrong no licence file found · pointer only · 19a5b88fdb546606 · report
AliTok ali-vilab/alitok/modeling/alitok.py official repository unverified no licence file found · pointer only · 9f49ed271158263c · report
Decoder ali-vilab/alitok/modeling/alitok.py official repository unverified no licence file found · pointer only · 71c21cc5683de18f · report
Encoder ali-vilab/alitok/modeling/alitok.py official repository unverified no licence file found · pointer only · c5d3142e52feb9a8 · report
VAutoencoder ali-vilab/alitok/modeling/alitok.py official repository unverified no licence file found · pointer only · ed9a3eb24c70f0c6 · report

Tasks

DecoderImage Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation ImageNet 256x256 AliTok-XL, autoregressive, 662M FID 1.35 #13 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 AliTok-XL, autoregressive, 662M Inception score 318.8 #13 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 AliTok-XL, autoregressive, 318M FID 1.42 #17 of 94 Archive leaderboard report
Image Generation ImageNet 256x256 AliTok-XL, autoregressive, 318M Inception score 326.6 #17 of 94 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

Diffusion

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