Radial Attention implements a sparse attention pattern designed to reduce the computational overhead of transformer attention in video generation models. It achieves near-linear complexity scaling, enabling single-GPU video generation in 33-90 seconds on H100/4090 GPUs. The project integrates with multiple video generation frameworks including Wan2.1, HunyuanVideo, and Mochi-1, and is compatible with techniques like SageAttention and LoRA for further optimization.