DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease

• URL: http://arxiv.org/abs/2603.25872v1
• Date: Thu, 26 Mar 2026 19:58:42 GMT
• Title: DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease
• Abstract summary: DRiffusion is a parallel sampling framework that parallelizes diffusion inference through a draft-and-refine process.<n> Empirically, DRiffusion attains 1.4$times$-3.7$times$ speedup across multiple diffusion models while incur minimal degradation in generation quality.
• Score: 3.5025591191253933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 aggressive mode is used, where $n$ denotes the number of devices. Empirically, DRiffusion attains 1.4$\times$-3.7$\times$ speedup across multiple diffusion models while incur minimal degradation in generation quality: on MS-COCO dataset, both FID and CLIP remain largely on par with those of the original model, while PickScore and HPSv2.1 show only minor average drops of 0.17 and 0.43, respectively. These results verify that DRiffusion delivers substantial acceleration and preserves perceptual quality.

Related papers

• DiffusionGemma Technical Report [72.10570774996219]
  We introduce DiffusionGemma, a language model that uses discrete diffusion to generate text at exceptionally high speed.<n>Rather than decoding one token at a time, DiffusionGemma iteratively refines blocks of 256 tokens in parallel.<n>Our compute-efficient two-stage training pipeline uses fewer than 10% of the starting AR model's total training token budget.
  arXiv  Detail & Related papers  (2026-07-31T16:11:46Z)
• Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling [55.388363730120325]
  We parallelize the mainstream $$-leaping algorithm for absorbing discrete diffusion in a Continuous-Time Markov Chain (CTMC) framework.<n>By leveraging the continuous-time integral form of the $$-leaping algorithm and the Picard method, we achieve parallel-in-time sampling acceleration.<n>Our research expands the potential of discrete diffusion models for efficient parallel inference, with broader implications for applications such as molecular structure and language generation.
  arXiv  Detail & Related papers  (2026-07-01T10:59:33Z)
• Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network [33.60166262384809]
  Dual-Rate Diffusion is a method to accelerate sampling by interleaving the execution of a heavy high-capacity context encoder and a light efficient denoising model.<n>On ImageNet benchmarks, Dual-Rate Diffusion matches the performance of standard baselines while reducing computational cost by a factor of $2$-$4$.
  arXiv  Detail & Related papers  (2026-05-18T10:30:02Z)
• Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling [10.012655130147413]
  Diffusion models have achieved remarkable progress in high-fidelity image, video, and audio generation.<n>Our framework achieves $2.31times$ and $2.07times$ latency reductions on SDXL and SD3, respectively.<n>Our approach also outperforms existing methods in acceleration under high-resolution synthesis settings.
  arXiv  Detail & Related papers  (2026-02-25T10:23:07Z)
• FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion [22.207275433870937]
  Diffusion language models offer parallel token generation and inherent bidirectionality.<n>State-of-the-art diffusion models (e.g., Dream 7B, LLaDA 8B) suffer from slow inference.<n>We introduce Guided Diffusion, a training-free method that uses a lightweight pretrained autoregressive model to supervise token unmasking.
  arXiv  Detail & Related papers  (2025-05-27T17:39:39Z)
• Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations [53.180374639531145]
  Self-Refining Diffusion Samplers (SRDS) retain sample quality and can improve latency at the cost of additional parallel compute.<n>We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations.
  arXiv  Detail & Related papers  (2024-12-11T11:08:09Z)
• $\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models [16.738569359216438]
  We present $textitJump Your Steps$ (JYS), a novel approach that optimize the allocation of discrete sampling timesteps by minimizing CDE without extra computational cost. In experiments across image, music, and text generation, JYS significantly improves sampling quality.
  arXiv  Detail & Related papers  (2024-10-10T09:44:25Z)
• AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising [49.785626309848276]
  AsyncDiff is a universal and plug-and-play acceleration scheme that enables model parallelism across multiple devices. For the Stable Diffusion v2.1, AsyncDiff achieves a 2.7x speedup with negligible degradation and a 4.0x speedup with only a slight reduction of 0.38 in CLIP Score. Our experiments also demonstrate that AsyncDiff can be readily applied to video diffusion models with encouraging performances.
  arXiv  Detail & Related papers  (2024-06-11T03:09:37Z)
• DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models [58.450152413700586]
  We introduce a soft absorbing state that facilitates the diffusion model in learning to reconstruct discrete mutations based on the underlying Gaussian space. We employ state-of-the-art ODE solvers within the continuous space to expedite the sampling process. Our proposed method effectively accelerates the training convergence by 4x and generates samples of similar quality 800x faster.
  arXiv  Detail & Related papers  (2023-10-09T15:29:10Z)
• Fast Diffusion Model [122.36693015093041]
  Diffusion models (DMs) have been adopted across diverse fields with their abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion Model (FDM) to significantly speed up DMs from a DM optimization perspective.
  arXiv  Detail & Related papers  (2023-06-12T09:38:04Z)
• Parallel Sampling of Diffusion Models [76.3124029406809]
  Diffusion models are powerful generative models but suffer from slow sampling. We present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel.
  arXiv  Detail & Related papers  (2023-05-25T17:59:42Z)
• Subspace Diffusion Generative Models [4.310834990284412]
  Score-based models generate samples by mapping noise to data (and vice versa) via a high-dimensional diffusion process. We restrict the diffusion via projections onto subspaces as the data distribution evolves toward noise. Our framework is fully compatible with continuous-time diffusion and retains its flexible capabilities.
  arXiv  Detail & Related papers  (2022-05-03T13:43:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.