Abstract: Recent advancements in training diffusion models have made generating high-quality videos possible. Particularly, the spatial-temporal diffusion transformers (ST-DiTs) emerge as a promising diffusion model architecture for generating videos of high-resolution (1080p) and long duration (20 seconds). However, the quadratic scaling of compute cost with respect to resolution and duration, primarily due to spatial-temporal attention layers processing longer sequences, results in high inference latency of ST-DiTs. This hinders their applicability in time-sensitive scenarios. Existing sequence parallelism techniques, such as DeepSpeed-Ulysses and RingAttention, are not optimally scalable for ST-DiT inference across multiple GPU machines due to cross-machine communication overheads. To address this challenge, we introduce ScaleFusion, a scalable inference engine designed to optimize ST-DiT inference for high-resolution, long video generation. By leveraging the inherent structure of spatial-temporal attention layers, ScaleFusion effectively hides cross-machine communication overhead through novel intra-layer and inter-layer communication scheduling algorithms. This enables strong scaling of 3.60$\times$ on 4 Amazon EC2 p4d.24xlarge machines (32 A100 GPUs) against 1 machine (8 A100 GPUs). Our experiments demonstrate that ScaleFusion outperforms state-of-the-art techniques, achieving an average speedup of 1.36$\times$ (up to 1.58$\times$).