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arXiv · 2512.24724

FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation

Jibin Song·Mingi Kwon·Jaeseok Jeong·Youngjung Uh
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Abstract

In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages, respectively. We further introduce simple criteria to choose stage boundaries and provide a velocity-divergence analysis as an effective proxy for identifying capacity-sensitive regions. Across LTX-Video (2B/13B) and WAN 2.1 (1.3B/14B), FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models. FlowBlending is also compatible with existing sampling-acceleration techniques, enabling up to 2x additional speedup. Project page is available at: https://jibin86.github.io/flowblending_project_page.

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Jibin Song, Mingi Kwon, Jaeseok Jeong, Youngjung Uh. 2025-12-31. FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation. https://arxiv.org/abs/2512.24724

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