Uni-Instruct: One-step Diffusion Model through Unified Diffusion
  Divergence Instruction
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.
6836a4401314d4ac39a526f3
Yifei Wang
claimed_verified
2025-05-29T07:43:51.221Z
6838043c11b8b14a21f6ecd8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/kzuNcUqFpRbM4rlHEfbrx.png
Yifei Wang
user
smallAI
6836a4401314d4ac39a526f4
Weimin Bai
6836a4401314d4ac39a526f5
Colin Zhang
6836a4401314d4ac39a526f6
Debing Zhang
6836a4401314d4ac39a526f7
Weijian Luo
6836a4401314d4ac39a526f8
He Sun
Uni-Instruct: One-step Diffusion Model through Unified Diffusion
  Divergence Instruction
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.
Uni-Instruct unifies and enhances one-step diffusion distillation methods through a novel diffusion expansion theory, achieving state-of-the-art performance in unconditional and conditional image generation and text-to-3D generation.
Uni-Instruct diffusion expansion theory f-divergence one-step diffusion models Frechet Inception Distance (FID) CIFAR10 ImageNet-64x64 text-to-3D generation SDS VSD