Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
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TL;DR - Context-Matched Distillation accelerates autoregressive video generation while ensuring teacher supervision obeys the student’s causal constraints. It achieves state-of-the-art aggregate performance and improves adherence to time-varying camera controls.
- Replaces bidirectional full-clip scoring with a causal teacher that cannot access future frames or controls.
- Prefix Scoring evaluates targets using the actual student-generated context that produced them.
- Prefix Corruption stabilizes early training by perturbing unreliable generated prefixes.
- Supports frame-wise, chunk-wise, long-video, and camera-conditioned distillation.
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Context-Matched Distillation: Teacher Causality for Autoregressive Video Distillation
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Hugging Face upvotes 19
TL;DR - Context-Matched Distillation accelerates autoregressive video generation while ensuring teacher supervision obeys the student’s causal constraints. It achieves state-of-the-art aggregate performance and improves adherence to time-varying camera controls.
- Replaces bidirectional full-clip scoring with a causal teacher that cannot access future frames or controls.
- Prefix Scoring evaluates targets using the actual student-generated context that produced them.
- Prefix Corruption stabilizes early training by perturbing unreliable generated prefixes.
- Supports frame-wise, chunk-wise, long-video, and camera-conditioned distillation.