In brief: AI video generation may soon no longer be limited to expensive subscriptions or high-powered servers. Thanks to a recent breakthrough, even a gaming laptop could generate full-length AI videos.
The breakthrough comes from Lvmin Zhang of GitHub and Maneesh Agrawala of Stanford University. The duo developed FramePack, a neural network architecture that enables high-quality video diffusion with as little as 6GB of VRAM. This is a significant achievement, especially given the model’s size – 13 billion parameters – which allows it to generate full 60-second clips at 30 FPS using only a mid-range GPU.
The key lies in how FramePack operates. Traditional video diffusion models rely on previously generated frames to predict the next one. As the video length increases, so does the “temporal context” – the number of past frames the model must consider – resulting in higher memory demands. This is why most models require 12GB of VRAM or more to run efficiently.
FramePack flips that on its head. Instead of letting memory usage balloon with longer clips, it compresses input frames based on importance into a fixed-length context, keeping the memory footprint compact and consistent regardless of video duration.
This innovation allows the model to process thousands of frames, even with large architectures, on laptop-grade GPUs. It also enables training with batch sizes comparable to those used in image diffusion models.
But FramePack doesn’t just reduce memory demands, it also addresses drifting – a common issue where video quality degrades over time. By using intelligent compression patterns and scheduling techniques, FramePack helps maintain visual consistency from beginning to end.
To top it off, the model includes a user-friendly GUI. Users can upload images, enter text prompts, and view a live preview as frames are generated. On an RTX 4090, optimized generation speeds reach up to 0.6 frames per second. Naturally, performance is lower on less powerful GPUs, but even an RTX 3060 can handle it.
Currently, FramePack supports Nvidia’s RTX 30, 40, and the new 50 series GPUs, provided they support FP16 or BF16 data formats. There’s no confirmed support yet for AMD or Intel GPUs, but the model works across multiple operating systems, including Linux.
You can find full model details and source code on GitHub.
