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Text-to-video generation has seen rapid progress thanks to large-scale diffusion models. However, maintaining generation quality while significantly reducing inference time remains a persistent challenge. The paper “Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation” introduces a clever solution: reuse internal model layers adaptively during inference—without retraining. This strategy allows for faster generation (1.63× improvement) while keeping the visual fidelity intact. In this post, we’ll break down the core idea behind Foresight, how it achieves speed without sacrificing quality, and why this could reshape efficient video generation pipelines in research and industry alike.