SALT: Self-Consistent Distribution Matching with Cache-Aware Training for Few-Step Video Generation

Distilling video generation models to extremely low infer-ence budgets (e.g., 2–4 NFEs) is crucial for real-time deployment, yetremains challenging. Trajectory-style consistency distillation often be-comes conservative under complex video dynamics, yielding an over-smoothed appearance and weak motion. Distribution matching distilla-tion (DMD) can recover sharp, mode-seeking samples, but its local train-ing signals do not explicitly regularize how denoising updates composeacross timesteps, making composed rollouts prone to drift. To overcomethis challenge, we propose Self-Consistent Distribution Matching Distil-lation (SC-DMD), which explicitly regularizes the endpoint-consistentcomposition of consecutive denoising updates. For real-time autoregres-sive video generation, we further treat the KV cache as a quality param-eterized condition and propose Cache-Distribution-Aware training. Thistraining scheme applies SC-DMD over multi-step rollouts and introducesa cache-conditioned feature alignment objective that steers low-qualityoutputs toward high-quality references. Across extensive experiments onboth non-autoregressive backbones (e.g., Wan 2.1) and autoregressivereal-time paradigms (e.g., Self Forcing), our method, dubbed Salt, con-sistently improves low-NFE video generation quality while remainingcompatible with diverse KV-cache memory mechanisms. Project page:https://xingtongge.github.io/Salt.