Diffusion transformers, crucial for high-fidelity video generation, face severe inference slowdown because long token sequences make attention computationally dominant. Training‑free dynamic sparse attention mitigates this by selectively computing only a subset of key‑value blocks. However, existing methods struggle to achieve both efficient and accurate sparsification due to rigid routing and unpredictable cost structures.