Uni-Instruct: One-step Diffusion Model through Unified Diffusion
  Divergence Instruction
Authors: name: Yifei Wang, name: Weimin Bai, name: Colin Zhang, name: Debing Zhang, name: Weijian Luo, name: He Sun

In this paper, we unify more than 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 \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 minimizing the expanded f-divergence family. The novel unification
introduced by Uni-Instruct not only offers new theoretical contributions that
help understand existing approaches from a high-level perspective but also
leads to state-of-the-art one-step diffusion generation performances. On the
CIFAR10 generation benchmark, Uni-Instruct achieves record-breaking Frechet
Inception Distance (FID) values of \emph{1.46} for unconditional
generation and \emph{1.38} for conditional generation. On the
ImageNet-64times 64 generation benchmark, Uni-Instruct achieves a new SoTA
one-step generation FID of \emph{1.02}, which outperforms its 79-step
teacher diffusion with a significant improvement margin of 1.33 (1.02 vs 2.35).
We also apply Uni-Instruct on broader tasks like text-to-3D generation. For
text-to-3D generation, Uni-Instruct gives decent results, which slightly
outperforms previous methods, such as SDS and VSD, in terms of both generation
quality and diversity. Both the solid theoretical and empirical contributions
of Uni-Instruct will potentially help future studies on one-step diffusion
distillation and knowledge transferring of diffusion models.