POSE: Phased One-Step Adversarial Equilibrium for Video Diffusion Models

• URL: http://arxiv.org/abs/2508.21019v1
• Date: Thu, 28 Aug 2025 17:20:01 GMT
• Title: POSE: Phased One-Step Adversarial Equilibrium for Video Diffusion Models
• Authors: Jiaxiang Cheng, Bing Ma, Xuhua Ren, Hongyi Jin, Kai Yu, Peng Zhang, Wenyue Li, Yuan Zhou, Tianxiang Zheng, Qinglin Lu,
• Abstract summary: POSE (Phased One-Step Equilibrium) is a distillation framework that reduces the sampling steps of large-scale video diffusion models.<n>We show that POSE outperforms other acceleration methods on VBench-I2V by average 7.15% in semantic alignment, temporal conference and frame quality.
• Score: 18.761042377485367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The field of video diffusion generation faces critical bottlenecks in sampling efficiency, especially for large-scale models and long sequences. Existing video acceleration methods adopt image-based techniques but suffer from fundamental limitations: they neither model the temporal coherence of video frames nor provide single-step distillation for large-scale video models. To bridge this gap, we propose POSE (Phased One-Step Equilibrium), a distillation framework that reduces the sampling steps of large-scale video diffusion models, enabling the generation of high-quality videos in a single step. POSE employs a carefully designed two-phase process to distill video models:(i) stability priming: a warm-up mechanism to stabilize adversarial distillation that adapts the high-quality trajectory of the one-step generator from high to low signal-to-noise ratio regimes, optimizing the video quality of single-step mappings near the endpoints of flow trajectories. (ii) unified adversarial equilibrium: a flexible self-adversarial distillation mechanism that promotes stable single-step adversarial training towards a Nash equilibrium within the Gaussian noise space, generating realistic single-step videos close to real videos. For conditional video generation, we propose (iii) conditional adversarial consistency, a method to improve both semantic consistency and frame consistency between conditional frames and generated frames. Comprehensive experiments demonstrate that POSE outperforms other acceleration methods on VBench-I2V by average 7.15% in semantic alignment, temporal conference and frame quality, reducing the latency of the pre-trained model by 100$\times$, from 1000 seconds to 10 seconds, while maintaining competitive performance.

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