Text-to-image (T2I) diffusion models have revolutionized visual content creation, but extending these capabilities to text-to-video (T2V) generation remains a challenge, particularly in preserving temporal consistency. Existing methods that aim to improve consistency often cause trade-offs such as reduced imaging quality and impractical computational time. To address these issues we introduce VideoGuide, a novel framework that enhances the temporal consistency of pretrained T2V models without the need for additional training or fine-tuning. Instead, VideoGuide leverages any pretrained video diffusion model (VDM) or itself as a guide during the early stages of inference, improving temporal quality by interpolating the guiding model's denoised samples into the sampling model's denoising process. The proposed method brings about significant improvement in temporal consistency and image fidelity, providing a cost-effective and practical solution that synergizes the strengths of various video diffusion models. Furthermore, we demonstrate prior distillation, revealing that base models can achieve enhanced text coherence by utilizing the superior data prior of the guiding model through the proposed method. Project Page: https://dohunlee1.github.io/videoguide.github.io/

本研究解决了文本到视频生成中时间一致性保持的难题，提出了一种名为VideoGuide的新框架，能够在无需额外训练或微调的情况下，提升预训练视频扩散模型的时间一致性。研究结果表明，该方法显著改善了时间一致性和图像质量，提供了一种高效且实用的解决方案，同时展示了基模型通过引导模型的优越数据先验实现增强的文本一致性的潜力。

视频引导：通过教师引导提高视频扩散模型的性能而无需训练