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ARXIV:2604.04451 · VIDEO GENERATION SERVING · SUBMITTED 07 APR · 20:13 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04451VIDEO GENERATION SERVINGSUBMITTED 07 APR · 20:13 UTCFRESHNESS UNKNOWNHao Liu · Ye Huang · Chenghuan Huang · Zhenyi Zheng · Jiangsu Du · Ziyang Ma · +2 at arXiv
Accelerate video diffusion model serving by reusing computation across inference requests.
Opportunity summary
Pain Accelerate video diffusion model serving by reusing computation across inference requests.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Accelerate video diffusion model serving by reusing computation across inference requests. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip redundant denoising steps.
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching approaches primarily exploit similarity within the diffusion process of…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Chorus achieves up to 45\% speedup on industrial 4-step distilled models, where prior intra-request caching approaches are ineffective.
Video Generation Serving moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Accelerate video diffusion model serving by reusing computation across inference requests.
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10.48550/arXiv.2604.04451Accelerate video diffusion model serving by reusing computation across inference requests.
Abstract
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip redundant denoising steps. In this paper, we introduce Chorus, a caching approach that leverages similarity across requests to accelerate video diffusion model serving. Chorus achieves up to 45\% speedup on industrial 4-step distilled models, where prior intra-request caching approaches are ineffective. Particularly, Chorus employs a three-stage caching strategy along the denoising process. Stage 1 performs full reuse of latent features from similar requests. Stage 2 exploits inter-request caching in specific latent regions during intermediate denoising steps. This stage is combined with Token-Guided Attention Amplification to improve semantic alignment between the generated video and the conditional prompts, thereby extending the applicability of full reuse to later denoising steps.
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What was readable
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Dimensions overall score 5.0
PROBLEM
Accelerate video diffusion model serving by reusing computation across inference requests. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip redundant denoising steps.
METHOD
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip re...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Chorus achieves up to 45\% speedup on industrial 4-step distilled models, where prior intra-request caching approaches are ineffective.
WHY NOW
Video Generation Serving moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Accelerate video diffusion model serving by reusing computation across inference requests. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip redundant denoising steps.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching approaches primarily exploit similarity within the diffusion process of a single request to skip redundant denoising steps.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Chorus achieves up to 45\% speedup on industrial 4-step distilled models, where prior intra-request caching approaches are ineffective.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Generation Serving moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Accelerate video diffusion model serving by reusing computation across inference requests.
Segment
Video Generation Serving
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Commercial read
5.0/10 public viability
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missing
reason
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proof status
unverified
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GitHub and Hugging Face maturity payloads
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Technical feasibility
partial
Current read
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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