We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlike conventional vector quantization, which assigns a discrete token to each patch feature vector, CVQ quantizes each channel of the feature map. This formulation represents an image as discrete levels of visual details, rather than as a grid of spatial patches. Based on CVQ, we introduce a new visual autoregressive framework with "next-channel prediction". Instead of rendering images patch by patch in raster order, our Channel-wise Autoregressive (CAR) model predicts image channels sequentially, producing progressively enriched visual details. Specifically, it first sketches global structure and then refines fine-grained attributes, akin to a human artist's workflow. Empirically, we show that: (1) CVQ achieves 100% codebook utilization with a 16K+ codebook size without any bells and whistles, and substantially improves reconstruction quality over conventional VQ; and (2) CAR attains a DPG score of 86.7 and a GenEval score of 0.79, demonstrating strong effectiveness for text-to-image generation.

翻译：我们提出通道向矢量量化（Channel-wise Vector Quantization, CVQ），这是一种新颖的图像标记化范式，用通道向标记取代了分块向标记。与传统的矢量量化（为每个分块特征向量分配一个离散标记）不同，CVQ对特征图的每个通道进行量化。这种表述将图像表示为视觉细节的离散层次，而非空间分块的网格。基于CVQ，我们引入了一种新的视觉自回归框架，采用"下一通道预测"机制。我们的通道向自回归（Channel-wise Autoregressive, CAR）模型并非以光栅顺序逐块渲染图像，而是按顺序预测图像通道，逐步生成丰富的视觉细节。具体而言，它首先勾勒全局结构，然后细化细节属性，类似于人类艺术家的创作流程。实验表明：（1）CVQ在无需任何额外技巧的情况下，实现了16K以上码本大小的100%码本利用率，并在重建质量上显著优于传统VQ；（2）CAR在DPG指标上达到86.7分，在GenEval指标上达到0.79分，展示了其在文本到图像生成中的强大有效性。