title: Uni-Instruct: One-step Diffusion Model through Unified Diffusion
    Divergence Instruction
summary: 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.
paper_authors._id: 6836a4401314d4ac39a526f3
paper_authors.name: Yifei Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:43:51.221Z
paper_authors._id: 6838043c11b8b14a21f6ecd8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/kzuNcUqFpRbM4rlHEfbrx.png
paper_authors.fullname: Yifei Wang
paper_authors.type: user
paper_authors.user: smallAI
paper_authors._id: 6836a4401314d4ac39a526f4
paper_authors.name: Weimin Bai
paper_authors._id: 6836a4401314d4ac39a526f5
paper_authors.name: Colin Zhang
paper_authors._id: 6836a4401314d4ac39a526f6
paper_authors.name: Debing Zhang
paper_authors._id: 6836a4401314d4ac39a526f7
paper_authors.name: Weijian Luo
paper_authors._id: 6836a4401314d4ac39a526f8
paper_authors.name: He Sun
paper_title: Uni-Instruct: One-step Diffusion Model through Unified Diffusion
    Divergence Instruction
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Uni-Instruct diffusion expansion theory f-divergence one-step diffusion models Frechet Inception Distance (FID) CIFAR10 ImageNet-64x64 text-to-3D generation SDS VSD