History-Guided Video Diffusion
Authors: name: Kiwhan Song, name: Boyuan Chen, name: Max Simchowitz, name: Yilun Du, name: Russ Tedrake, name: Vincent Sitzmann

Classifier-free guidance (CFG) is a key technique for improving conditional
generation in diffusion models, enabling more accurate control while enhancing
sample quality. It is natural to extend this technique to video diffusion,
which generates video conditioned on a variable number of context frames,
collectively referred to as history. However, we find two key challenges to
guiding with variable-length history: architectures that only support
fixed-size conditioning, and the empirical observation that CFG-style history
dropout performs poorly. To address this, we propose the Diffusion Forcing
Transformer (DFoT), a video diffusion architecture and theoretically grounded
training objective that jointly enable conditioning on a flexible number of
history frames. We then introduce History Guidance, a family of guidance
methods uniquely enabled by DFoT. We show that its simplest form, vanilla
history guidance, already significantly improves video generation quality and
temporal consistency. A more advanced method, history guidance across time and
frequency further enhances motion dynamics, enables compositional
generalization to out-of-distribution history, and can stably roll out
extremely long videos. Website: https://boyuan.space/history-guidance