title: Inductive Moment Matching
summary: Diffusion models and Flow Matching generate high-quality samples but are slow
  at inference, and distilling them into few-step models often leads to
  instability and extensive tuning. To resolve these trade-offs, we propose
  Inductive Moment Matching (IMM), a new class of generative models for one- or
  few-step sampling with a single-stage training procedure. Unlike distillation,
  IMM does not require pre-training initialization and optimization of two
  networks; and unlike Consistency Models, IMM guarantees distribution-level
  convergence and remains stable under various hyperparameters and standard model
  architectures. IMM surpasses diffusion models on ImageNet-256x256 with 1.99 FID
  using only 8 inference steps and achieves state-of-the-art 2-step FID of 1.98
  on CIFAR-10 for a model trained from scratch.
paper_authors._id: 67cfa0532c3c436eccedab57
paper_authors.name: Linqi Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:42:39.700Z
paper_authors._id: 64554c3c9d37c3fb332975ed
paper_authors.avatarUrl: /avatars/13001d48b56cca99471a7e75116b70cf.svg
paper_authors.fullname: Linqi Zhou
paper_authors.type: user
paper_authors.user: alexzhou907
paper_authors._id: 67cfa0532c3c436eccedab58
paper_authors.name: Stefano Ermon
paper_authors._id: 67cfa0532c3c436eccedab59
paper_authors.name: Jiaming Song
paper_title: Inductive Moment Matching
paper_summary: Diffusion models and Flow Matching generate high-quality samples but are slow
  at inference, and distilling them into few-step models often leads to
  instability and extensive tuning. To resolve these trade-offs, we propose
  Inductive Moment Matching (IMM), a new class of generative models for one- or
  few-step sampling with a single-stage training procedure. Unlike distillation,
  IMM does not require pre-training initialization and optimization of two
  networks; and unlike Consistency Models, IMM guarantees distribution-level
  convergence and remains stable under various hyperparameters and standard model
  architectures. IMM surpasses diffusion models on ImageNet-256x256 with 1.99 FID
  using only 8 inference steps and achieves state-of-the-art 2-step FID of 1.98
  on CIFAR-10 for a model trained from scratch.
paper_ai_keywords: Diffusion models Flow Matching inference instability distillation Inductive Moment Matching (IMM) generative models one-step sampling few-step sampling single-stage training procedure pre-training initialization Consistency Models distribution-level convergence hyperparameters ImageNet-256x256 FID CIFAR-10