TrajeQ: Trajectory-Preserving Flow Matching Quantization for Low-Bit Video Generation

While flow matching (FM) has emerged as a prominent paradigm for high-quality video generation, its significant computational overhead presents a major obstacle to practical implementation. Although post-training quantization (PTQ) is an established approach for efficient generation, existing PTQ methods primarily minimize reconstruction error in a magnitude-centric manner. This objective is misaligned with deterministic FM trajectories, where low-bit quantization introduces velocity-field orientation errors that can compound across integration steps and cause deviations from the full-precision trajectory. To address this issue, we introduce ***TrajeQ*** to preserve the geometric integrity of the velocity field and reduce trajectory drift under low-bit quantization. Our approach introduces *Curvature-Adaptive Quantization (CAQ)* to detect angular shifts across timesteps and mitigate geometric distortion via adaptive precision. We also integrate *Directional Bottleneck Preservation (DBP)*, a sensitivity-guided precision assignment scheme that retains only the most vulnerable layers in full precision. Experiments demonstrate that ***TrajeQ*** achieves state-of-the-art VBench performance and maintains stable video generation under extreme W4A4 and W3A6 quantization regimes where existing methods degrade severely. We provide the code in the supplementary material and additional video results at https://anonymous-orange.github.io/.