Accelerating Speculative Diffusions via Block Verification

• URL: http://arxiv.org/abs/2606.13426v1
• Date: Thu, 11 Jun 2026 14:54:13 GMT
• Title: Accelerating Speculative Diffusions via Block Verification
• Abstract summary: Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme.<n>Existing diffusion adaptations either use inefficient sampling techniques or rely on an alternative scheme.<n>We introduce a novel scheme that efficiently implements the original speculative sampling mechanism for diffusion models.
• Score: 77.23389794645801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speculative decoding speeds up LLM inference by using a draft model to generate tokens, with an acceptance-rejection scheme that ensures that the output matches the target distribution. Adapting this to continuous diffusions is difficult because speculative sampling requires drawing from a residual distribution. While straightforward in discrete spaces, efficiently sampling this residual in continuous space is non-trivial. Consequently, existing diffusion adaptations either use computationally inefficient sampling techniques or rely on an alternative scheme. In this work, we introduce a novel scheme that efficiently implements the original speculative sampling mechanism for diffusion models. Our approach offers a critical advantage over current methods: it enables us to adapt block verification from LLMs to diffusions -- which provably improves the acceptance rate of drafts. Furthermore, we formalize and analyze the Free Drafter, a heuristic self-speculative drafter for diffusions that requires no training. By enabling block verification, our Free Drafter yields up to a 6.3% speedup over existing speculative methods with no additional training and negligible overhead beyond the existing parallel verification pass.

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