ZEUS: Accelerating Diffusion Models with Only Second-Order Predictor
arXiv:2604.01552v1 Announce Type: new Abstract: Denoising generative models deliver high-fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required during sampling. Training-free acceleration methods reduce latency by either sparsifying the model architecture or shortening the sampling trajectory. Current training-free acceleration methods are more complex than necessary: higher-order predictors amplify error under aggressive speedups, and architectural modifications hinder deployment. Beyond 2x acceleration, step skipping creates structural scarcity -- at most one fresh evaluation per local window -- leaving the computed output and its backward difference as the only causally grounded information. Based on this, we propose ZEUS, an acceleration method that predicts reduced denoiser evaluations using a second-order predictor, and stabilizes aggressive consecutive skipping with an interleaved scheme that avoids back-to-back extrap
arXiv:2604.01552v1 Announce Type: new Abstract: Denoising generative models deliver high-fidelity generation but remain bottlenecked by inference latency due to the many iterative denoiser calls required during sampling. Training-free acceleration methods reduce latency by either sparsifying the model architecture or shortening the sampling trajectory. Current training-free acceleration methods are more complex than necessary: higher-order predictors amplify error under aggressive speedups, and architectural modifications hinder deployment. Beyond 2x acceleration, step skipping creates structural scarcity -- at most one fresh evaluation per local window -- leaving the computed output and its backward difference as the only causally grounded information. Based on this, we propose ZEUS, an acceleration method that predicts reduced denoiser evaluations using a second-order predictor, and stabilizes aggressive consecutive skipping with an interleaved scheme that avoids back-to-back extrapolations. ZEUS adds essentially zero overhead, no feature caches, and no architectural modifications, and it is compatible with different backbones, prediction objectives, and solver choices. Across image and video generation, ZEUS consistently improves the speed-fidelity performance over recent training-free baselines, achieving up to 3.2x end-to-end speedup while maintaining perceptual quality. Our code is available at: https://github.com/Ting-Justin-Jiang/ZEUS.
Executive Summary
The article introduces ZEUS, a novel acceleration method for diffusion models that leverages a second-order predictor to reduce denoiser evaluations without introducing architectural modifications or significant overhead. ZEUS addresses a critical bottleneck in diffusion model inference—latency caused by iterative denoiser calls—by enabling aggressive skipping of evaluations while mitigating structural scarcity through an interleaved scheme. Unlike prior training-free acceleration methods, ZEUS avoids the error amplification issues of higher-order predictors and does not require complex architectural changes, making it broadly compatible with diverse backbones and objectives. Empirical results demonstrate consistent improvements in speed-fidelity trade-offs, achieving up to 3.2x acceleration while preserving perceptual quality, marking a significant advancement in diffusion model efficiency.
Key Points
- ▸ ZEUS uses a second-order predictor to reduce denoiser evaluations
- ▸ Avoids architectural modifications and overhead
- ▸ Mitigates structural scarcity via an interleaved scheme
Merits
Compatibility
ZEUS is broadly compatible with different model backbones, prediction objectives, and solver choices without requiring architectural changes.
Performance Gain
Achieves up to 3.2x end-to-end speedup while maintaining perceptual quality, outperforming recent training-free baselines.
Demerits
Generalization Risk
While promising, the method’s long-term applicability across diverse generative domains (e.g., audio, 3D) remains untested and requires further validation.
Expert Commentary
ZEUS represents a sophisticated yet elegant solution to a persistent problem in diffusion model inference. The decision to leverage a second-order predictor rather than higher-order alternatives is particularly noteworthy, as it circumvents the error amplification trap that has plagued prior acceleration schemes. The interleaved scheme’s ability to stabilize consecutive skipping without compromising causal grounding is a subtle but critical innovation—it preserves the integrity of gradient information while enabling substantial speedups. Notably, the absence of feature caches or architectural overhead renders ZEUS not only technically elegant but also highly deployable in production environments. This positions ZEUS as a paradigm-shifting contribution, not merely an incremental improvement. It may catalyze a shift in how training-free acceleration is conceived—moving from architectural tweaks to predictive modeling as the primary lever. The open-source availability of the code further enhances reproducibility and adoption, elevating ZEUS beyond a theoretical novelty to a practical standard.
Recommendations
- ✓ Researchers should integrate ZEUS as a benchmark in comparative studies of diffusion model acceleration.
- ✓ Industry practitioners deploying diffusion models in latency-sensitive applications should evaluate ZEUS for potential integration into production pipelines.
Sources
Original: arXiv - cs.LG
