Minute-Long Videos with Dual Parallelisms
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.
683670a816cb1e8ad3235e45
Zeqing Wang
683670a816cb1e8ad3235e46
Bowen Zheng
683670a816cb1e8ad3235e47
Xingyi Yang
683670a816cb1e8ad3235e48
Yuecong Xu
683670a816cb1e8ad3235e49
Xinchao Wang
Minute-Long Videos with Dual Parallelisms
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.
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.
Diffusion Transformer (DiT) video diffusion models distributed inference DualParal parallelization temporal frames model layers block-wise denoising asynchronous computation feature cache coordinated noise initialization