Accelerating Diffusion Sampling via Speculative Draft Trees

2026-09-15

AI Summary

通过引入基于相对熵编码的投机草稿树和贪婪拒绝采样，加速扩散模型生成，减少目标评估次数。

Abstract

Speculative sampling accelerates diffusion model generation by drafting inexpensive candidate states and correcting them under a coupling that preserves the target distribution exactly, reducing the number of expensive target evaluations. Existing diffusion samplers, notably those based on reflection maximal coupling, are topologically constrained: their lookahead drafts form a chain graph, a single linear sequence, which inherently limits the acceptance rate per target evaluation. We connect speculative sampling in diffusion models to relative entropy coding (REC). This perspective shows the lookahead need not be linear and motivates our central contribution, draft trees, which enrich the candidates considered per round and lower the target function evaluations. We further adopt greedy rejection sampling, an REC algorithm, as the draft-target coupling, improving acceptance while guaranteeing exact target samples. Experiments across diverse target and draft models demonstrate up to 8.3% acceleration over the reflection coupling baseline in practical settings.

Problem

• diffusion model
• speculative sampling
• reflection maximal coupling
• relative entropy coding
• draft trees

Innovation

• speculative sampling
• draft trees
• relative entropy coding
• greedy rejection sampling