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arXiv · 2607.15585

SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation

Zihao Zhang·Haoyu Zhao·Siqian Yang·Yidi Wu·Yudong Jiang·Zuxuan Wu
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Abstract

Despite the success of diffusion models in Video Frame Interpolation (VFI), existing methods still suffer from two critical limitations. First, latent diffusion inevitably loses fine-grained details when reconstructing images from latent representations back to the pixel space. Second, multi-step sampling incurs prohibitive memory consumption and inference latency. To address these issues, we propose SPEED, a one-step pixel diffusion framework for high-quality VFI. Specifically, SPEED employs a progressive multi-stage architecture with dynamic patch scaling to effectively learn multi-scale motion, structural, and appearance representations. Furthermore, we propose a novel Noise-Update-Only Attention mechanism to prevent semantic degradation of the clean condition frames while reducing the computational overhead by nearly 50%. Besides, we introduce a Drift-aware Timestep Sampling strategy coupled with a tailored training objective to directly predict images in the pixel space, enabling one-step inference without compromising the quality of the generated frames. Extensive experiments show that SPEED achieves state-of-the-art performance. On SNU-FILM, SPEED reduces LPIPS by 8.8% while delivering 63.3% faster inference and 10.6% lower memory usage. On challenging 4K benchmarks, it further surpasses prior methods by up to 51.5% in LPIPS.

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Zihao Zhang, Haoyu Zhao, Siqian Yang, Yidi Wu, Yudong Jiang, Zuxuan Wu. 2026-07-17. SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation. https://arxiv.org/abs/2607.15585

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