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arXiv · 2503.07565

Inductive Moment Matching

Linqi Zhou·Stefano Ermon·Jiaming Song
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Abstract

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.

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Linqi Zhou, Stefano Ermon, Jiaming Song. 2025-03-10. Inductive Moment Matching. https://arxiv.org/abs/2503.07565

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