Inductive Moment Matching

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 Moment Matching Self-Distillation (MMSD), a new class of generative models for one- or few-step sampling with a single-stage training procedure. Unlike distillation, MMSD does not require pre-training initialization and optimization of two networks; and unlike Consistency Models, MMSD guarantees distribution-level convergence and remains stable under various hyperparameters and standard model architectures. MMSD surpasses diffusion models on ImageNet-256x256 with 2. 13 FID using only 8 inference steps and achieves state-of-the-art 2-step FID of 2. 05 on CIFAR-10 for a model trained from scratch.

Authors

• Linqi Zhou
• Stefano Ermon
• Jiaming Song

Keywords

• generative models
• diffusion models
• flow matching
• moment matching
• consistency models