No Training, No Problem: Rethinking Classifier-Free Guidance for
  Diffusion Models
Authors: name: Seyedmorteza Sadat, name: Manuel Kansy, name: Otmar Hilliges, name: Romann M. Weber

Classifier-free guidance (CFG) has become the standard method for enhancing
the quality of conditional diffusion models. However, employing CFG requires
either training an unconditional model alongside the main diffusion model or
modifying the training procedure by periodically inserting a null condition.
There is also no clear extension of CFG to unconditional models. In this paper,
we revisit the core principles of CFG and introduce a new method, independent
condition guidance (ICG), which provides the benefits of CFG without the need
for any special training procedures. Our approach streamlines the training
process of conditional diffusion models and can also be applied during
inference on any pre-trained conditional model. Additionally, by leveraging the
time-step information encoded in all diffusion networks, we propose an
extension of CFG, called time-step guidance (TSG), which can be applied to any
diffusion model, including unconditional ones. Our guidance techniques are easy
to implement and have the same sampling cost as CFG. Through extensive
experiments, we demonstrate that ICG matches the performance of standard CFG
across various conditional diffusion models. Moreover, we show that TSG
improves generation quality in a manner similar to CFG, without relying on any
conditional information.