提出一种因果图像分词方法，实现高效且高保真的图像生成。
CaTok: Taming Mean Flows for One-Dimensional Causal Image Tokenization
- 通过时间区间选择与均值流目标，构建一维因果表示。
- 在ImageNet上达0.75 FID、22.53 PSNR和0.674 SSIM，训练更高效。
- 适合追求快速生成与高质量图像重建的研究者。
自回归语言模型依赖因果分词，但将这一范式扩展到视觉仍具挑战。现有视觉分词器或直接展平2D块形成非因果序列，或采用启发式排序，与“下一个词预测”模式不匹配。近期扩散自编码器同样存在缺陷：解码器依赖所有标记缺乏因果性，嵌套丢弃机制则引入不平衡。为此，本文提出CaTok，一种基于均值流解码器的一维因果图像分词器。通过在时间区间内选择标记并绑定至均值流目标，如图1所示，CaTok学习因果一维表示，支持快速单步生成与高保真多步采样，同时自然捕捉各标记区间内的多样化视觉概念。为进一步稳定并加速训练，我们提出简单正则化REPA-A，使编码器特征与视觉基础模型（VFMs）对齐。实验表明，CaTok在ImageNet重建上达到最优性能，0.75 FID、22.53 PSNR和0.674 SSIM，仅需更少训练轮次，且自回归模型性能可媲美领先方法。
原文摘要 · Abstract (English)
Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into non-causal sequences or enforce heuristic orderings that misalign with the "next-token prediction" pattern. Recent diffusion autoencoders similarly fall short: conditioning the decoder on all tokens lacks causality, while applying nested dropout mechanism introduces imbalance. To address these challenges, we present CaTok, a 1D causal image tokenizer with a MeanFlow decoder. By selecting tokens over time intervals and binding them to the MeanFlow objective, as illustrated in Fig. 1, CaTok learns causal 1D representations that support both fast one-step generation and high-fidelity multi-step sampling, while naturally capturing diverse visual concepts across token intervals. To further stabilize and accelerate training, we propose a straightforward regularization REPA-A, which aligns encoder features with Vision Foundation Models (VFMs). Experiments demonstrate that CaTok achieves state-of-the-art results on ImageNet reconstruction, reaching 0.75 FID, 22.53 PSNR and 0.674 SSIM with fewer training epochs, and the AR model attains performance comparable to leading approaches.
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