title: Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling
summary: Diffusion models have emerged as a powerful tool for generating high-quality
  images, videos, and 3D content. While sampling guidance techniques like CFG
  improve quality, they reduce diversity and motion. Autoguidance mitigates these
  issues but demands extra weak model training, limiting its practicality for
  large-scale models. In this work, we introduce Spatiotemporal Skip Guidance
  (STG), a simple training-free sampling guidance method for enhancing
  transformer-based video diffusion models. STG employs an implicit weak model
  via self-perturbation, avoiding the need for external models or additional
  training. By selectively skipping spatiotemporal layers, STG produces an
  aligned, degraded version of the original model to boost sample quality without
  compromising diversity or dynamic degree. Our contributions include: (1)
  introducing STG as an efficient, high-performing guidance technique for video
  diffusion models, (2) eliminating the need for auxiliary models by simulating a
  weak model through layer skipping, and (3) ensuring quality-enhanced guidance
  without compromising sample diversity or dynamics unlike CFG. For additional
  results, visit https://junhahyung.github.io/STGuidance.
paper_authors._id: 674dcfe2aef5b4ffeca9afb2
paper_authors.name: Junha Hyung
paper_authors._id: 674dcfe2aef5b4ffeca9afb3
paper_authors.name: Kinam Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-02T16:11:06.066Z
paper_authors._id: 64797735a68454566356b708
paper_authors.avatarUrl: /avatars/3424d022dd8ad29b56eb41814c5c3dee.svg
paper_authors.fullname: Kinam Kim
paper_authors.type: user
paper_authors.user: kinam0252
paper_authors._id: 674dcfe2aef5b4ffeca9afb4
paper_authors.name: Susung Hong
paper_authors._id: 674dcfe2aef5b4ffeca9afb5
paper_authors.name: Min-Jung Kim
paper_authors._id: 674dcfe2aef5b4ffeca9afb6
paper_authors.name: Jaegul Choo
paper_title: Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling
paper_summary: Diffusion models have emerged as a powerful tool for generating high-quality
  images, videos, and 3D content. While sampling guidance techniques like CFG
  improve quality, they reduce diversity and motion. Autoguidance mitigates these
  issues but demands extra weak model training, limiting its practicality for
  large-scale models. In this work, we introduce Spatiotemporal Skip Guidance
  (STG), a simple training-free sampling guidance method for enhancing
  transformer-based video diffusion models. STG employs an implicit weak model
  via self-perturbation, avoiding the need for external models or additional
  training. By selectively skipping spatiotemporal layers, STG produces an
  aligned, degraded version of the original model to boost sample quality without
  compromising diversity or dynamic degree. Our contributions include: (1)
  introducing STG as an efficient, high-performing guidance technique for video
  diffusion models, (2) eliminating the need for auxiliary models by simulating a
  weak model through layer skipping, and (3) ensuring quality-enhanced guidance
  without compromising sample diversity or dynamics unlike CFG. For additional
  results, visit https://junhahyung.github.io/STGuidance.
paper_ai_keywords: diffusion models CFG Autoguidance Spatiotemporal Skip Guidance (STG) transformer-based video diffusion models implicit weak model self-perturbation spatiotemporal layers sample quality sample diversity dynamics