Single-Step Consistent Diffusion Samplers

• URL: http://arxiv.org/abs/2502.07579v1
• Date: Tue, 11 Feb 2025 14:25:52 GMT
• Title: Single-Step Consistent Diffusion Samplers
• Authors: Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang,
• Abstract summary: Existing sampling algorithms typically require many iterative steps to produce high-quality samples. We introduce consistent diffusion samplers, a new class of samplers designed to generate high-fidelity samples in a single step. We show that our approach yields high-fidelity samples using less than 1% of the network evaluations required by traditional diffusion samplers.
• Score: 8.758218443992467
• License:
• Abstract: Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterative steps to produce high-quality samples, leading to high computational costs that limit their practicality in time-sensitive or resource-constrained settings. In this work, we introduce consistent diffusion samplers, a new class of samplers designed to generate high-fidelity samples in a single step. We first develop a distillation algorithm to train a consistent diffusion sampler from a pretrained diffusion model without pre-collecting large datasets of samples. Our algorithm leverages incomplete sampling trajectories and noisy intermediate states directly from the diffusion process. We further propose a method to train a consistent diffusion sampler from scratch, fully amortizing exploration by training a single model that both performs diffusion sampling and skips intermediate steps using a self-consistency loss. Through extensive experiments on a variety of unnormalized distributions, we show that our approach yields high-fidelity samples using less than 1% of the network evaluations required by traditional diffusion samplers.

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