Ms. Forcing: Efficient Streaming Video Generation with Multi-Scale Patchification and Attention

Streaming video diffusion models have made substantial progress toward interactive and dynamic world simulation, but the nested autoregressive and denoising loops of conventional next-frame generation hinder real-time deployment. Recent rolling-window methods pipeline denoising across multiple consecutive frames at different noise levels, improving throughput and long-horizon stability. However, they tokenize every state at the same fine spatial granularity, leaving substantial noise-dependent redundancy in the joint denoising window. We propose Ms. Forcing, which adapts spatial granularity to each state's noise level for efficient streaming video generation. Its Multi-Scale Patchification (MSP) assigns coarser patches to noisier states, reducing the active-window token count by 45%, while Multi-Scale Self-Attention (MSSA) matches the density of visible non-sink keys and values to each query scale to further reduce attention cost. Fixing both schedules by window position preserves a static, hardware-friendly computation graph in Ms. Forcing. We further reduce the training–inference mismatch in rolling-window DMD through Homogeneous-Noise-Level DMD (H-DMD), which assembles each fake video from clean predictions sharing the same source noise level. The multi-scale design helps offset the additional training cost of backpropagating through overlapping windows. We include both quantitative and qualitative experiments to show that Ms. Forcing reaches 22.84 FPS on a single H200 GPU, 39.7% faster than Rolling Forcing, while significantly improving VBench scores in both short video and long video generation settings.