n 10 existing one-step diffusion distillation
approaches, such as Diff-Instruct, DMD, SIM, SiD, $f$-distill, etc, inside a
theory-driven framework which we name the \textbf{\emph{Uni-Instruct}}.
Uni-Instruct is motivated by our proposed diffusion expansion theory of the
$f$-divergence family. Then we introduce key theories that overcome the
intractability issue of the original expanded $f$-divergence, resulting in an
equivalent yet tractable loss that effectively trains one-step diffusion models
by minimizi