CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models
Authors: name: Weichen Fan, name: Amber Yijia Zheng, name: Raymond A. Yeh, name: Ziwei Liu

Classifier-Free Guidance (CFG) is a widely adopted technique in
diffusion/flow models to improve image fidelity and controllability. In this
work, we first analytically study the effect of CFG on flow matching models
trained on Gaussian mixtures where the ground-truth flow can be derived. We
observe that in the early stages of training, when the flow estimation is
inaccurate, CFG directs samples toward incorrect trajectories. Building on this
observation, we propose CFG-Zero*, an improved CFG with two contributions: (a)
optimized scale, where a scalar is optimized to correct for the inaccuracies in
the estimated velocity, hence the * in the name; and (b) zero-init, which
involves zeroing out the first few steps of the ODE solver. Experiments on both
text-to-image (Lumina-Next, Stable Diffusion 3, and Flux) and text-to-video
(Wan-2.1) generation demonstrate that CFG-Zero* consistently outperforms CFG,
highlighting its effectiveness in guiding Flow Matching models. (Code is
available at github.com/WeichenFan/CFG-Zero-star)