Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law.
Recent tree-based methods allocate the parallel compute budget more effectively than single-chain drafts, as demonstrated by Diffusion Greedy Rejection Sampling (D-GRS).
D-GRS generates $K$ conditionally independent candidates per node, and sequentially tests them in their generation order.
Yet the sampled candidates admit an informative ranking without additional target-model evaluations.
To exploit this, we introduce Rank-Aware Speculative Sampling (RASS), a verification rule for speculative draft trees based on rank-aware list coupling.
RASS orders draft candidates along the proposal-target mean displacement and samples a rank with weights optimized to minimize total variation between the selected-proposal and target laws.
Finally, the selected candidate is maximally coupled with the target, with residual correction ensuring exact sampling for any choice of rank weights.
We evaluate RASS on a Gaussian-mixture target, unconditional pixel-space generation on FFHQ, conditional generation on CIFAR-10, and latent diffusion with Stable Diffusion 3.5 using COCO2014 prompts.
Measured by the ratio of standard to speculative sampling's target-model evaluation counts, RASS improves on D-GRS across the evaluated settings, with gains reaching approximately 20% on CIFAR-10 at matched compute budgets.