DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease
Diffusion models have achieved remarkable success in generating high-fidelity content but suffer from slow, iterative sampling, resulting in high latency that limits their use in interactive applications. We introduce DRiffusion, a parallel sampling framework that parallelizes diffusion inference through a draft-and-refine process. DRiffusion employs skip transitions to generate multiple draft states for future timesteps and computes their corresponding noises in parallel, which are then used in the standard denoising process to produce refined results. Theoretically, our method achieves an acceleration rate of $\tfrac{1}{n}$ or $\tfrac{2}{n+1}$, depending on whether the conservative or a...
| Authors | Runsheng Bai, Chengyu Zhang, Yangdong Deng |
|---|---|
| Primary category | cs.LG |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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