In recent times, video generative models have emerged as a focal point of attention, unlocking a realm of fresh creative opportunities. Despite this, the velocity of these models remains a significant obstacle to their broader adoption. State-of-the-art generative models, while impressive in their capabilities, are hampered by sluggishness and computational demands due to their iterative diffusion processes. To address this issue, in a new paper AnimateDif