Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder practical deployment. Although both quantization and sparsity can independently accelerate inference while maintaining generation quality, naively combining these techniques in existing training-free approaches leads to significant performance degradation, as they fail to achieve proper joint optimization. We introduce FPSAttention, a novel training-aware co-design of FP8 quantization and Sparsity for video generation, with a focus on the 3D bi-directional attention mechanism. Our approach features three key innovations: 1) A unified 3D tile-wise granularity that simultaneously supports both quantization and sparsity. 2) A denoising step-aware strategy that adapts to the noise schedule, addressing the strong correlation between quantization/sparsity errors and denoising steps. 3) A native, hardware-friendly kernel that leverages FlashAttention and is implemented with optimized Hopper architecture features, enabling highly efficient execution. Trained on Wan2.1's 1.3B and 14B models and evaluated on the vBench benchmark, FPSAttention achieves a 7.09$\times$ kernel speedup for attention operations and a 4.96$\times$ end-to-end speedup for video generation compared to the BF16 baseline at 720p resolution—without sacrificing generation quality.

Accurate and timely monitoring of wildfires is crucial for reducing property damage and casualties. In recent years, advances in satellite technology have enabled the comprehensive, timely, and rapid recording of various abrupt events on the Earth’s surface. However, achieving a balance between temporal and spatial resolution remains a significant challenge for remote sensing, hindering the quick and accurate detection of wildfires. This letter proposes a novel framework for the near real-time monitoring of wildfire coupled with the BRDF model and deep learning technology, which enables near real-time detection of wildfire by assessing the degree to which the observed value of geostationary satellite image deviates from the predicted theoretical observation value. The experimental results show that the proposed method is capable of effectively detecting wildfires in near real-time. Moreover, the encouraging results suggest that the method holds promise for monitoring the spread of wildfire to a certain extent.