The leak of anomalous information from input condition poses a great challenge to reconstruction-based anomaly detection.Recent diffusion-based methods respond to this issue by suppressing anomaly information for condition injection or in-sampling inversion.However, since they treat conditions as a time-invariant prior, they fall into a tradeoff problem between anomaly suppression and normal pattern consistency.To address this probelm, we propose Debiasing Trace Guidance (DTG) framework based on Flow Matching towards debiasing generation for more accurate unsupervised anomaly detection.Generally, DTG distills a low-dimensional generation sub-trace robust to anomalies by Top-down Trace Distillation, and then utilizes its time-varying velocity features to guide a debiasing generation by Bottom-up Velocity Alignment.The trace distillation filters out high-frequency anomalies via learnable wavelet filters and reserving structural information by keeping global consistency across samples using Skinhorn Distance.Subsequently, the velocity field of original trace is aligned with the one of sub-trace through KV-Injection Attention mechanism.The model is forced to generate normal details from corresponding low-dimensional contexts via Alignment Mask.Experimental results on several benchmarks have demonstrated the effectiveness of the proposed method.