Pith. sign in

REVIEW 2 cited by

FlexCache: Flexible Approximate Cache System for Video Diffusion

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2501.04012 v1 pith:2YI752IC submitted 2024-12-18 cs.MM cs.LG

classification cs.MMcs.LG
keywords cachediffusionapproximatesystemvideocomputationalflexcachemodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a single video. While prior research has addressed the computational overhead in text-to-image diffusion models, the techniques developed are not directly suitable for video diffusion models due to the significantly larger cache requirements and enhanced computational demands associated with video generation. We present FlexCache, a flexible approximate cache system that addresses the challenges in two main designs. First, we compress the caches before saving them to storage. Our compression strategy can reduce 6.7 times consumption on average. Then we find that the approximate cache system can achieve higher hit rate and computation savings by decoupling the object and background. We further design a tailored cache replacement policy to support the two techniques mentioned above better. Through our evaluation, FlexCache reaches 1.26 times higher throughput and 25% lower cost compared to the state-of-the-art diffusion approximate cache system.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DSTAR: Accelerating Diffusion Transformers via Spatial and Temporal Redundancy Reduction

    cs.AR 2026-07 conditional novelty 6.0 of 10

    DSTAR reports 7.33x latency speedup and 41.89x energy savings over an A100 GPU on seven diffusion transformers by quantizing differential activations to as few as 2 bits and reusing block-wise sparse attention scores.

  2. ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering

    cs.GR 2025-06 conditional novelty 5.0 of 10

    Caching deep encoder features across frames, with a SMAPE-threshold refresh policy, yields about 1.4x average inference speedup on three real-time rendering networks with small perceptual loss at the high-sensitivity setting.

Pith tools