WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens

Building a unified visual tokenizer is essential for bridgingthe gap between visual understanding and generation. Yet existing ap-proaches struggle with the inherent conflict between these tasks, as asingle token space is forced to support both high-level semantic abstrac-tion and low-level pixel reconstruction. We propose WinTok, a concisehybrid tokenizer that achieves a win-win performance by explicitly de-coupling the two objectives. WinTok supplements pixel tokens with aset of learnable semantic tokens, effectively mitigating cross-task inter-ference without incurring the computational overhead of dual tokenizers.To further enhance understanding capability, we introduce an asymmet-ric token distillation mechanism: the semantic tokens are guided by pre-trained semantic embeddings from any visual foundation model, enablingthem to inherit strong discriminative power while maintaining flexibility.Across 10 challenging benchmarks, WinTok delivers consistent improve-ments in reconstruction, understanding, and generation. Trained on only50M open-source data, WinTok surpasses the strong baseline UniTok by11.2% in classification accuracy and achieves a competitive reconstruc-tion rFID of 0.41, despite using substantially less training data. Code isreleased at https://github.com/WeChatCV/WinTok.