基于对抗自蒸馏的单步因果视频生成
Towards One-step Causal Video Generation via Adversarial Self-Distillation
AI总结：
本文提出基于蒸馏的高效因果视频生成框架，通过对抗自蒸馏策略提升生成质量，结合首帧增强策略减少误差传播，实验表明在单步和双步视频生成中优于现有方法。
摘要：
近期混合视频生成模型结合自回归时间动态与扩散基空间去噪，但其序列迭代性质导致误差累积和推理时间长。本文提出基于蒸馏的框架，通过对抗自蒸馏策略实现高质生成，结合首帧增强策略减少误差传播，实验表明在单步和双步视频生成中优于现有方法。
英文摘要：
Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads to error accumulation and long inference times. In this work, we propose a distillation-based framework for efficient causal video generation that enables high-quality synthesis with extremely limited denoising steps. Our approach builds upon the Distribution Matching Distillation (DMD) framework and proposes a novel Adversarial Self-Distillation (ASD) strategy, which aligns the outputs of the student model's n-step denoising process with its (n+1)-step version at the distribution level. This design provides smoother supervision by bridging small intra-student gaps and more informative guidance by combining teacher knowledge with locally consistent student behavior, substantially improving training stability and generation quality in extremely few-step scenarios (e.g., 1-2 steps). In addition, we present a First-Frame Enhancement (FFE) strategy, which allocates more denoising steps to the initial frames to mitigate error propagation while applying larger skipping steps to later frames. Extensive experiments on VBench demonstrate that our method surpasses state-of-the-art approaches in both one-step and two-step video generation. Notably, our framework produces a single distilled model that flexibly supports multiple inference-step settings, eliminating the need for repeated re-distillation and enabling efficient, high-quality video synthesis.
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