title: VideoGuide: Improving Video Diffusion Models without Training Through a
    Teacher's Guide
summary: 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/
paper_authors._id: 6704b04606af66752cd6a20a
paper_authors.name: Dohun Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-08T09:05:33.908Z
paper_authors._id: 665b04fdef08dd8cf4b7579e
paper_authors.avatarUrl: /avatars/02694064a1508503a6b58f2c5b9e74ae.svg
paper_authors.fullname: Dohun Lee
paper_authors.type: user
paper_authors.user: DHCAI
paper_authors._id: 6704b04606af66752cd6a20b
paper_authors.name: Bryan S Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-08T08:17:25.231Z
paper_authors._id: 6628efe14e1fa854f48d3a28
paper_authors.avatarUrl: /avatars/aa5421149a07a82b5c2a25978f9b6926.svg
paper_authors.fullname: Sangwoo Kim
paper_authors.type: user
paper_authors.user: bryanswkim
paper_authors._id: 6704b04606af66752cd6a20c
paper_authors.name: Geon Yeong Park
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-08T08:17:26.806Z
paper_authors._id: 64cfff0abf39f9c8bebd2a6f
paper_authors.avatarUrl: /avatars/e3405136c8af5065c287eff718c78b5b.svg
paper_authors.fullname: Geon Yeong Park
paper_authors.type: user
paper_authors.user: geonyoung-park
paper_authors._id: 6704b04606af66752cd6a20d
paper_authors.name: Jong Chul Ye
paper_title: VideoGuide: Improving Video Diffusion Models without Training Through a
    Teacher's Guide
paper_summary: 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/
paper_ai_summary: VideoGuide enhances temporal consistency and image fidelity in text-to-video generation by leveraging pretrained video diffusion models during the inference process without additional training.
paper_ai_keywords: text-to-image models text-to-video models temporal consistency pretrained video diffusion models denoised samples sampling model prior distillation text coherence