VideoGuide: Improving Video Diffusion Models without Training Through a
  Teacher's Guide
Authors: name: Dohun Lee, name: Bryan S Kim, name: Geon Yeong Park, name: Jong Chul Ye

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: http://videoguide2025.github.io/