Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling
Authors: name: Junha Hyung, name: Kinam Kim, name: Susung Hong, name: Min-Jung Kim, name: Jaegul Choo

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.