Unified multimodal models typically rely on pretrained vision encoders and use separate visual representations for understanding and generation, creating misalignment between the two tasks and preventing fully end-to-end optimization from raw pixels. We introduce Tuna-2, a native unified multimodal model that performs visual understanding and generation directly based on pixel embeddings. Tuna-2 drastically simplifies the model architecture by employing simple patch embedding layers to encode visual input, completely discarding the modular vision encoder designs such as the VAE or the representation encoder. Experiments show that Tuna-2 achieves state-of-the-art performance in multimodal benchmarks, demonstrating that unified pixel-space modelling can fully compete with latent-space approaches for high-quality image generation. Moreover, while the encoder-based variant converges faster in early pretraining, Tuna-2's encoder-free design achieves stronger multimodal understanding at scale, particularly on tasks requiring fine-grained visual perception. These results show that pretrained vision encoders are not necessary for multimodal modelling, and end-to-end pixel-space learning offers a scalable path toward stronger visual representations for both generation and perception.

翻译：统一的多模态模型通常依赖预训练的视觉编码器，并为理解与生成任务使用分离的视觉表示，这导致两项任务之间存在对齐偏差，且无法实现从原始像素到输出的完全端到端优化。我们提出Tuna-2——一种原生统一的多模态模型，能够直接基于像素嵌入执行视觉理解与生成。Tuna-2通过采用简单的图像块嵌入层编码视觉输入，彻底简化了模型架构，完全摒弃了VAE或表示编码器等模块化视觉编码器设计。实验表明，Tuna-2在多模态基准测试中达到最先进性能，证明统一的像素空间建模能够与基于潜在空间的高质量图像生成方法全面竞争。此外，尽管基于编码器的变体在预训练初期收敛更快，但Tuna-2的无编码器设计在更大规模上实现了更强的多模态理解能力，尤其适用于需要细粒度视觉感知的任务。这些结果表明，预训练的视觉编码器并非多模态建模的必要条件，而端到端的像素空间学习为生成与感知任务提供了可扩展的强视觉表征路径。