ViSPI: Efficient 2D Parallelism for Sparse Long-Sequence Video DiT Inference

Diffusion Transformers (DiTs) have become a dominant architecture for video generation. However, scaling DiTs to longer videos is challenging as rapidly growing sequence lengths exacerbate the quadratic cost of full self-attention. While sparse attention reduces computational overhead by selectively enabling token interactions, it fundamentally changes the execution characteristics of DiTs, introducing irregular workloads and communication patterns. Existing distributed parallel strategies designed for dense computation do not adapt well to these sparse workloads. To address this challenge, we present ViSPI, a distributed inference framework tailored for sparse video DiTs. Specifically, it models hardware-level execution costs to balance heterogeneous attention workloads across GPUs. Meanwhile, it overlaps irregular Key-Value (KV) communication with computation via asynchronous execution. Experiments on representative video DiT models demonstrate that ViSPI significantly improves inference efficiency over state-of-the-art distributed inference frameworks while preserving generation quality and scaling efficiently across multiple GPUs.