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ARXIV:2604.01552 · GENERATIVE MODELS ACCELERATION · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.01552GENERATIVE MODELS ACCELERATIONSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEYixiao Wang · Ting Jiang · Zishan Shao · Hancheng Ye · Jingwei Sun · Mingyuan Ma · +3 at arXiv
ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality.
Opportunity summary
Pain ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality.
Evidence 0 refs | 0 sources | 67% coverage
Blocker Evidence unverified
ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality. Training-free acceleration methods reduce latency by either sparsifying the model architecture or…
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…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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…
Generative Models Acceleration moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality.
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Paper Pack
10.48550/arXiv.2604.01552ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality.
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.
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Viability
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Dimensions overall score 7.0
PROBLEM
ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality. Training-free acceleration methods reduce latency by either sparsifying the model architecture or shortening t...
METHOD
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 shorteni...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. 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 quali...
WHY NOW
Generative Models Acceleration moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
achieving up to 3.2x end-to-end speedup while maintaining perceptual quality
Directly stated in abstract with specific numeric result
partial
propose ZEUS, an acceleration method that predicts reduced denoiser evaluations using a second-order predictor
Directly stated in abstract as core method description
partial
ZEUS adds essentially zero overhead, no feature caches, and no architectural modifications
Explicitly stated in abstract with clear description of minimal overhead
partial
it is compatible with different backbones, prediction objectives, and solver choices
Directly stated in abstract as a key feature
partial
Current training-free acceleration methods are more complex than necessary
Stated in abstract but requires some interpretation of 'more complex than necessary'
partial
higher-order predictors amplify error under aggressive speedups
Directly stated in abstract as a limitation of existing methods
partial
stabilizes aggressive consecutive skipping with an interleaved scheme that avoids back-to-back extrapolations
Directly stated in abstract as key technical approach
partial
Beyond 2x acceleration, step skipping creates structural scarcity -- at most one fresh evaluation per local window
Technical claim stated in abstract but requires some domain knowledge to fully interpret
partial
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ZEUS accelerates diffusion model inference by up to 3.2x using a novel second-order predictor without architectural changes or training, maintaining perceptual quality.
Segment
Generative Models Acceleration
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