UniFlow: A Unified Pixel Flow Tokenizer for Visual Understanding and Generation

📌 核心贡献

提出 UniFlow 统一 tokenizer，通过 layer-wise adaptive self-distillation 在预训练视觉编码器上同时保留语义特征（用于理解）和细粒度细节（用于生成），有效缓解理解与生成之间的表征冲突。轻量级 patch-wise pixel flow decoder 以语义特征为条件，通过条件流匹配从噪声状态高效重建像素，ICLR 2026 收录，7B 版本在理解和生成双端均达到 SOTA。

📖 摘要

Tokenizer is a crucial component for both visual understanding and generation. To advance toward the ultimate goal of universal modeling, recent research has focused on developing a unified tokenizer. However, existing tokenizers face a significant performance trade-off between understanding and generation, stemming from the inherent conflict between high-level semantic abstraction and low-level pixel reconstruction. To tackle this challenge, we propose a generic and unified tokenizer, namely UniFlow, by flexibly adapting any visual encoder with a concise reconstruction decoder. Specifically, we introduce layer-wise adaptive self-distillation applied to the well-pretrained visual encoders, which enables UniFlow to simultaneously inherit the strong semantic features for visual understanding and flexibly adapt to model fine-grained details for visual generation. Moreover, we propose a lightweight patch-wise pixel flow decoder, which efficiently achieves high-fidelity pixel reconstruction by modeling a conditional flow from the noisy state back to the patch-wise pixel domain. By leveraging the semantic features as visual conditions for the decoder, we effectively alleviate the training conflicts between understanding and generation. Furthermore, the patch-wise learning strategy simplifies the data distribution, thereby improving training efficiency. Extensive experiments across 13 challenging benchmarks spanning 7 widely studied visual understanding and generation tasks demonstrate that UniFlow achieves a win-win outcome. For instance, our 7B UniFlow-XL not only surpasses the 14B TokenFlow-XL by 6.05% on average understanding benchmarks, but also achieves a competitive results in both visual reconstruction and generation, surpassing UniTok by 0.15 in rFID and 0.09 in gFID (without guidance), respectively.

🔍 关键信息

字段	内容
机构
发表	2025-10-12
分类	cs.CV
链接	arXiv

📝 我的笔记

🔗 相关论文

基于/改进自： VQGAN, UniTok 对比基线： VQGAN, UniTok, FSQ 同方向： AToken, VidTok, TokenFlow