Flow-Q: Flow Consistency for Post-Training Quantization of Vision Transformers

We present Flow-Q, a general post-training quantization framework for Vision Transformers. Our analysis shows that the existing sensitivity-weighted reconstruction paradigm induces feature distortion during quantization, degrading the robustness of quantized feature representations. To overcome this challenge, we first propose an evolution-aware learning (EAL) strategy that captures feature evolution information to construct a manifold representation with global topological structure. Moreover, a geometric constraint rectification (GCR) module is designed to utilize the manifold representation to rectify quantized features from both direction and magnitude perspectives, thereby alleviating feature distortion. EAL provides the geometric structural foundation for GCR, enabling the quantized model to inherit the robustness of visual representations from the full-precision network. Extensive experiments on nine tasks across twelve benchmarks demonstrate the strong generalizability of Flow-Q, which achieves new state-of-the-art quantization performance across DINOv3, conventional ViTs, and mobile-efficient models.