DiTango: Cost-Effective Parallel Diffusion Generation with Selective Attention State Reuse

arXiv:2607.15650v1 Announce Type: new Abstract: Recent advances in AI-generated content have driven widespread adoption of Diffusion Transformers (DiTs) for high-resolution, long-duration content generation. While parallelization techniques accelerate diffusion inference, they face significant scalability challenges due to excessive communication overhead in multi-node environments. We observe that sequence partitions in Context Parallelism (CP) exhibit distinct heterogeneity: spatially proximat...