上下文匹配蒸餾：教師因果性於自迴歸影片蒸餾

摘要

互動式自迴歸影片生成需要同時滿足低延遲展開與精確的線上控制。少步蒸餾透過減少去噪步驟來加速生成，而線上控制則引入了因果約束：幀與區塊只應依賴於生成時可得的歷史與控制訊號。然而，現有的影片分布匹配蒸餾（DMD）流程，常以對完整片段評分的雙向教師來監督因果少步學生模型。因此，對某個目標的評分可能依賴於學生生成該目標時尚不可得的未來幀與控制訊號，使得教師監督與學生的因果資訊集合不一致。我們提出上下文匹配蒸餾（CMD），這是一個因果 DMD 框架，能讓教師監督與生成每個目標時可得的資訊對齊。CMD 以因果教師取代雙向的整段片段評分；該教師在無法取得未來幀或控制訊號的情況下評估每個目標。相同的因果教師也用來初始化少步學生模型，從而在教師訓練、學生蒸餾與推論之間建立一致的因果表述。除了對齊時間資訊邊界之外，前綴評分進一步將監督對齊至學生實際展開的上下文，也就是在每個目標所依據的快取學生生成前綴之下評估該目標。前綴破壞則透過擾動訓練早期產生的不可靠前綴來穩定訓練，同時保留目標與上下文之間的對齊。憑藉簡單的因果表述，CMD 自然可延伸至逐幀與逐區塊生成、長影片蒸餾，以及相機條件蒸餾。實驗結果顯示，CMD 在短影片與長影片基準上，於自迴歸方法中達成整體最先進的表現，並大幅提升對時變相機控制指令的遵循度。

English

Interactive autoregressive video generation demands both low-latency rollouts and precise online control. Few-step distillation accelerates generation by reducing denoising steps, while online control imposes a causal constraint: frames and blocks should depend on history and controls available during generation. Existing video distribution matching distillation (DMD) pipelines, however, often supervise causal few-step students using bidirectional teachers that score complete clips. The score for a target can therefore depend on future frames and controls that were unavailable when the student generated it, misaligning teacher supervision with the student's causal information set. We introduce Context-Matched Distillation (CMD), a causal DMD framework that aligns teacher supervision with the information available when each target is generated. CMD replaces bidirectional full-clip scoring with a causal teacher that evaluates each target without access to future frames or controls. The same causal teacher initializes the few-step student, establishing a consistent causal formulation across teacher training, student distillation, and inference. Beyond aligning the temporal information boundary, Prefix Scoring matches supervision to the student's realized rollout context by evaluating each target under the cached student-generated prefix that produced it. Prefix Corruption further stabilizes training by perturbing unreliable prefixes produced early in training while preserving this target-context alignment. With a simple causal formulation, CMD naturally extends to frame-wise and chunk-wise generation, long video distillation, and camera-conditioned distillation. Experiments demonstrate state-of-the-art aggregate performance among autoregressive methods on both short- and long-video benchmarks, together with substantially improved adherence to time-varying camera controls.