Budget-Constrained Step-Level Diffusion Caching

• URL: http://arxiv.org/abs/2606.13496v1
• Date: Thu, 11 Jun 2026 15:45:05 GMT
• Title: Budget-Constrained Step-Level Diffusion Caching
• Abstract summary: Existing methods make per-step cache decisions using threshold-baseds, without directly optimizing for final output quality.<n>We propose BudCache, which fixes the compute budget in advance and search for the cache policy that best preserves the final output.<n>BudCache achieves better generation quality than deterministic caching baselines under the same inference budgets.
• Score: 9.784100090561045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Step-level caching accelerates diffusion models by exploiting temporal redundancy across denoising steps. Existing methods make per-step cache decisions using threshold-based heuristics, without directly optimizing for final output quality. As a result, their inference latency varies across inputs and is difficult to control at deployment. In this work, we propose BudCache, which inverts this formulation: rather than letting per-step error thresholds dictate the runtime cost, we fix the compute budget in advance and search for the cache policy that best preserves the final output. To tackle the combinatorial complexity of step selection, we combine Simulated Annealing with deterministic Hill Climbing. This offline search identifies high-quality cache policies within minutes and introduces no online search or thresholding overhead during inference. When the compute budget is very tight, we further introduce cache-aware schedule alignment, which adapts the time discretization to the selected cache policy to reduce cache-induced trajectory mismatch. Experiments on FLUX.1-dev and Wan2.1 show that BudCache achieves better generation quality than heuristic caching baselines under the same inference budgets. Code is available at https://github.com/Westlake-AGI-Lab/BudCache

Related papers

• EpaCache: Error-Propagation-Aware Caching for Accelerating Diffusion-Based Visual Generation [30.438361624564553]
  Caching-based methods reduce inference latency by reusing intermediate computations across adjacent timesteps.<n>EpaCache is a training-free caching policy that adaptively allocates the reuse budget on timesteps with lower downstream impact.<n>EpaCache consistently improves the latency--fidelity trade-off over existing caching methods.
  arXiv  Detail & Related papers  (2026-08-29T13:38:11Z)
• ReCache: Learning Budget-Aware Caching Schedules for Diffusion Models via REINFORCE [40.4732814295941]
  ReCache learns the recomputation schedule that maximizes generation quality.<n>It trains via policy gradients, sidestepping backpagation through full diffusion inference.<n>It consistently outperforms scheduling baselines.
  arXiv  Detail & Related papers  (2026-06-04T11:59:56Z)
• WorldCache: Content-Aware Caching for Accelerated Video World Models [50.7543797435026]
  We present textbfWorldCache, a Perception-Constrained Dynamic CaCache framework.<n>WorldCache improves both when and how to reuse features.<n>On Cosmos-2.5-2B evaluated on PAI-Bench, WorldCache achieves textbf$2.3times$ inference speedup while preserving textbf99.4% of baseline quality.
  arXiv  Detail & Related papers  (2026-03-23T17:59:54Z)
• PreciseCache: Precise Feature Caching for Efficient and High-fidelity Video Generation [35.47114707080758]
  High computational costs and slow inference hinder the practical application of video generation models.<n>We propose textbfPreciseCache, a plug-and-play framework that precisely detects and skips truly redundant computations.
  arXiv  Detail & Related papers  (2026-03-01T08:08:49Z)
• 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)
• MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference [11.934900617930774]
  MeanCache is a training-free caching framework for efficient Flow Matching inference.<n>We demonstrate that MeanCache achieves 4.12X and 4.56X and 3.59X acceleration, respectively, while consistently outperforming state-of-the-art caching baselines in generation quality.
  arXiv  Detail & Related papers  (2026-01-27T08:35:50Z)
• 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)
• 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)
• Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching [56.286064975443026]
  We make an interesting and somehow surprising observation: the computation of a large proportion of layers in the diffusion transformer, through a caching mechanism, can be readily removed even without updating the model parameters. We introduce a novel scheme, named Learningto-Cache (L2C), that learns to conduct caching in a dynamic manner for diffusion transformers. Experimental results show that L2C largely outperforms samplers such as DDIM and DPM-r, alongside prior cache-based methods at the same inference speed.
  arXiv  Detail & Related papers  (2024-06-03T18:49:57Z)
• Accelerating Deep Learning Classification with Error-controlled Approximate-key Caching [72.50506500576746]
  We propose a novel caching paradigm, that we named approximate-key caching. While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error. We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching.
  arXiv  Detail & Related papers  (2021-12-13T13:49:11Z)

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.