EFlow: Fast Few-Step Video Generator Training from Scratch via Efficient Solution Flow

Scaling video diffusion transformers is fundamentally bottle-necked by two compounding costs: the expensive quadratic complexityof attention per step, and the iterative sampling steps. In this work, wepropose EFlow, a efficient few-step training framework, that tacklesthese bottlenecks simultaneously. To reduce sampling steps, we build ona solution-flow objective that learns a function mapping a noised state attime t to time s. Making this formulation computationally feasible andhigh-quality at video scale, however, demands two complementary innova-tions. First, we propose Gated Local–Global Attention, a token-droppablehybrid block which is efficient, expressive, and remains highly stable underaggressive random token-dropping, substantially reducing per-step com-pute. Second, we develop an efficient few-step training recipe. We proposePath-Drop Guided training to replace the expensive guidance target witha computationally cheap, weak path. Furthermore, we augment this witha Mean-Velocity Additivity regularizer to ensure high fidelity at extremelylow step counts. Together, our EFlow enables a practical from-scratchtraining pipeline, achieving up to 2.5× higher training throughput overstandard solution-flow, and 45.3× lower inference latency than standarditerative models with competitive performance on Kinetics and large-scaletext-to-video datasets.