You can generate text 1.7-4.4x faster from diffusion models by proposing tokens speculatively and verifying them in parallel, while mathematically guaranteeing the output distribution stays identical to sequential generation.
SpecCTMC speeds up text generation from masked diffusion language models by speculatively proposing multiple token updates in parallel, then verifying them exactly without changing the output distribution. Unlike typical speedup methods, it requires no separate draft model and mathematically guarantees it preserves the original sampling behavior.