title: Minute-Long Videos with Dual Parallelisms
summary: Diffusion Transformer (DiT)-based video diffusion models generate
  high-quality videos at scale but incur prohibitive processing latency and
  memory costs for long videos. To address this, we propose a novel distributed
  inference strategy, termed DualParal. The core idea is that, instead of
  generating an entire video on a single GPU, we parallelize both temporal frames
  and model layers across GPUs. However, a naive implementation of this division
  faces a key limitation: since diffusion models require synchronized noise
  levels across frames, this implementation leads to the serialization of
  original parallelisms. We leverage a block-wise denoising scheme to handle
  this. Namely, we process a sequence of frame blocks through the pipeline with
  progressively decreasing noise levels. Each GPU handles a specific block and
  layer subset while passing previous results to the next GPU, enabling
  asynchronous computation and communication. To further optimize performance, we
  incorporate two key enhancements. Firstly, a feature cache is implemented on
  each GPU to store and reuse features from the prior block as context,
  minimizing inter-GPU communication and redundant computation. Secondly, we
  employ a coordinated noise initialization strategy, ensuring globally
  consistent temporal dynamics by sharing initial noise patterns across GPUs
  without extra resource costs. Together, these enable fast, artifact-free, and
  infinitely long video generation. Applied to the latest diffusion transformer
  video generator, our method efficiently produces 1,025-frame videos with up to
  6.54times lower latency and 1.48times lower memory cost on 8timesRTX
  4090 GPUs.
paper_authors._id: 683670a816cb1e8ad3235e45
paper_authors.name: Zeqing Wang
paper_authors._id: 683670a816cb1e8ad3235e46
paper_authors.name: Bowen Zheng
paper_authors._id: 683670a816cb1e8ad3235e47
paper_authors.name: Xingyi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T07:28:50.831Z
paper_authors._id: 634cfebc350bcee9bed20a4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
paper_authors.fullname: Xingyi Yang
paper_authors.type: user
paper_authors.user: adamdad
paper_authors._id: 683670a816cb1e8ad3235e48
paper_authors.name: Yuecong Xu
paper_authors._id: 683670a816cb1e8ad3235e49
paper_authors.name: Xinchao Wang
paper_title: Minute-Long Videos with Dual Parallelisms
paper_summary: Diffusion Transformer (DiT)-based video diffusion models generate
  high-quality videos at scale but incur prohibitive processing latency and
  memory costs for long videos. To address this, we propose a novel distributed
  inference strategy, termed DualParal. The core idea is that, instead of
  generating an entire video on a single GPU, we parallelize both temporal frames
  and model layers across GPUs. However, a naive implementation of this division
  faces a key limitation: since diffusion models require synchronized noise
  levels across frames, this implementation leads to the serialization of
  original parallelisms. We leverage a block-wise denoising scheme to handle
  this. Namely, we process a sequence of frame blocks through the pipeline with
  progressively decreasing noise levels. Each GPU handles a specific block and
  layer subset while passing previous results to the next GPU, enabling
  asynchronous computation and communication. To further optimize performance, we
  incorporate two key enhancements. Firstly, a feature cache is implemented on
  each GPU to store and reuse features from the prior block as context,
  minimizing inter-GPU communication and redundant computation. Secondly, we
  employ a coordinated noise initialization strategy, ensuring globally
  consistent temporal dynamics by sharing initial noise patterns across GPUs
  without extra resource costs. Together, these enable fast, artifact-free, and
  infinitely long video generation. Applied to the latest diffusion transformer
  video generator, our method efficiently produces 1,025-frame videos with up to
  6.54times lower latency and 1.48times lower memory cost on 8timesRTX
  4090 GPUs.
paper_ai_summary: A distributed inference strategy, DualParal, is proposed to address high processing latency and memory costs in diffusion transformer-based video diffusion models by parallelizing frames and layers across GPUs with a block-wise denoising scheme and feature cache.
paper_ai_keywords: Diffusion Transformer (DiT) video diffusion models distributed inference DualParal parallelization temporal frames model layers block-wise denoising asynchronous computation feature cache coordinated noise initialization