Unified Multimodal Large Language Models (MLLMs) require a visual representation that simultaneously supports high-fidelity reconstruction, complex semantic extraction, and generative suitability. However, existing visual tokenizers typically struggle to satisfy these conflicting objectives within a single framework. In this paper, we introduce UniWeTok, a unified discrete tokenizer designed to bridge this gap using a massive binary codebook ($\mathit{2^{128}}$). For training framework, we introduce Pre-Post Distillation and a Generative-Aware Prior to enhance the semantic extraction and generative prior of the discrete tokens. In terms of model architecture, we propose a convolution-attention hybrid architecture with the SigLu activation function. SigLu activation not only bounds the encoder output and stabilizes the semantic distillation process but also effectively addresses the optimization conflict between token entropy loss and commitment loss. We further propose a three-stage training framework designed to enhance UniWeTok's adaptability cross various image resolutions and perception-sensitive scenarios, such as those involving human faces and textual content. On ImageNet, UniWeTok achieves state-of-the-art image generation performance (FID: UniWeTok 1.38 vs. REPA 1.42) while requiring a remarkably low training compute (Training Tokens: UniWeTok 33B vs. REPA 262B). On general-domain, UniWeTok demonstrates highly competitive capabilities across a broad range of tasks, including multimodal understanding, image generation (DPG Score: UniWeTok 86.63 vs. FLUX.1 [Dev] 83.84), and editing (GEdit Overall Score: UniWeTok 5.09 vs. OmniGen 5.06). We release code and models to facilitate community exploration of unified tokenizer and MLLM.

翻译：统一多模态大语言模型需要一种能够同时支持高保真重建、复杂语义提取以及生成适用性的视觉表示。然而，现有的视觉分词器通常难以在单一框架内满足这些相互冲突的目标。本文提出UniWeTok，一种利用大规模二进制码本（$\mathit{2^{128}}$）来弥合这一差距的统一离散分词器。在训练框架方面，我们引入前后蒸馏与生成感知先验，以增强离散令牌的语义提取能力和生成先验。在模型架构方面，我们提出了一种结合卷积与注意力机制的混合架构，并采用SigLu激活函数。SigLu激活不仅能够限制编码器输出、稳定语义蒸馏过程，还能有效解决令牌熵损失与承诺损失之间的优化冲突。我们进一步提出一个三阶段训练框架，旨在增强UniWeTok在不同图像分辨率以及感知敏感场景（如涉及人脸和文本内容）下的适应性。在ImageNet上，UniWeTok实现了最先进的图像生成性能（FID：UniWeTok 1.38 vs. REPA 1.42），同时所需的训练计算量极低（训练令牌数：UniWeTok 330亿 vs. REPA 2620亿）。在通用领域，UniWeTok在包括多模态理解、图像生成（DPG分数：UniWeTok 86.63 vs. FLUX.1 [Dev] 83.84）和编辑（GEdit综合分数：UniWeTok 5.09 vs. OmniGen 5.06）在内的广泛任务中展现出极具竞争力的能力。我们发布了代码和模型，以促进社区对统一分词器及多模态大语言模型的探索。