SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models

• URL: http://arxiv.org/abs/2602.18993v1
• Date: Sun, 22 Feb 2026 00:48:03 GMT
• Title: SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models
• Authors: Jiwoo Chung, Sangeek Hyun, MinKyu Lee, Byeongju Han, Geonho Cha, Dongyoon Wee, Youngjun Hong, Jae-Pil Heo,
• Abstract summary: We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that reuses decisions on a spectrally aligned representation.<n>Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware filter that preserves content-relevant components while suppressing noise.<n>Experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.
• Score: 41.7269767513774
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by caching and reusing intermediate outputs based on feature distances between adjacent timesteps. However, existing caching strategies typically rely on raw feature differences that entangle content and noise. This design overlooks spectral evolution, where low-frequency structure appears early and high-frequency detail is refined later. We introduce Spectral-Evolution-Aware Cache (SeaCache), a training-free cache schedule that bases reuse decisions on a spectrally aligned representation. Through theoretical and empirical analysis, we derive a Spectral-Evolution-Aware (SEA) filter that preserves content-relevant components while suppressing noise. Employing SEA-filtered input features to estimate redundancy leads to dynamic schedules that adapt to content while respecting the spectral priors underlying the diffusion model. Extensive experiments on diverse visual generative models and the baselines show that SeaCache achieves state-of-the-art latency-quality trade-offs.

Related papers

• SenCache: Accelerating Diffusion Model Inference via Sensitivity-Aware Caching [75.02865981328509]
  Caching reduces computation by reusing previously computed model outputs across timesteps.<n>We propose Sensitivity-Aware Caching (SenCache), a dynamic caching policy that adaptively selects caching timesteps on a per-sample basis.<n>SenCache achieves better visual quality than existing caching methods under similar computational budgets.
  arXiv  Detail & Related papers  (2026-02-27T17:36:09Z)
• Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation [45.717539734334906]
  Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates.<n>We show that existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible influence on the final generation.<n>We propose Spectral Evolution Search (SES), a plug-and-play framework for initial noise optimization that executes gradient-free evolutionary search within a low-frequency subspace.
  arXiv  Detail & Related papers  (2026-02-03T07:19:39Z)
• Forecast the Principal, Stabilize the Residual: Subspace-Aware Feature Caching for Efficient Diffusion Transformers [9.698781486878206]
  Diffusion Transformer (DiT) models have achieved unprecedented quality in image and video generation, yet their iterative sampling process remains computationally prohibitive.<n>We propose SVD-Cache, a subspace-aware caching framework that decomposes diffusion features via Singular Value Decomposition (SVD)<n>Our code is in supplementary material and will be released on Github.
  arXiv  Detail & Related papers  (2026-01-12T10:30:12Z)
• DiCache: Let Diffusion Model Determine Its Own Cache [62.954717254728166]
  DiCache is a training-free adaptive caching strategy for accelerating diffusion models at runtime.<n>Online Probe Profiling Scheme leverages a shallow-layer online probe to obtain an on-the-fly indicator for the caching error in real time.<n> Dynamic Cache Trajectory Alignment approximates the deep-layer feature output from multi-step historical caches.
  arXiv  Detail & Related papers  (2025-08-24T13:30:00Z)
• Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion Models [41.11005178050448]
  ProfilingDiT is a novel adaptive caching strategy that explicitly disentangles foreground and background-focused blocks.<n>Our framework achieves significant acceleration while maintaining visual fidelity across comprehensive quality metrics.
  arXiv  Detail & Related papers  (2025-04-04T03:30:15Z)
• FEB-Cache: Frequency-Guided Exposure Bias Reduction for Enhancing Diffusion Transformer Caching [10.760030872557374]
  Diffusion Transformer (DiT) has exhibited impressive generation capabilities but faces great challenges due to its high computational complexity.<n>In this paper, we first confirm that the cache greatly amplifies the exposure bias, resulting in a decline in the generation quality.<n>We introduce FEB-Cache, a joint caching strategy that aligns with the non-exposed bias diffusion process.
  arXiv  Detail & Related papers  (2025-03-10T09:49:18Z)
• Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model [55.64316746098431]
  Timestep Embedding Aware Cache (TeaCache) is a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps.<n>TeaCache achieves up to 4.41x acceleration over Open-Sora-Plan with negligible degradation of visual quality.
  arXiv  Detail & Related papers  (2024-11-28T12:50:05Z)
• DeepCache: Accelerating Diffusion Models for Free [65.02607075556742]
  DeepCache is a training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models. Under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS.
  arXiv  Detail & Related papers  (2023-12-01T17:01:06Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [80.20339155618612]
  DiffusionAD is a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network.<n>A rapid one-step denoising paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality.<n>Considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)

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.