Framepack
Lvmin Zhang (lllyasviel), Stanford University · Video
FramePack is an open-source video model that turns next-frame prediction into something a regular GPU can handle. It's built by Lvmin Zhang (lllyasviel) at Stanford. Its main trick is compressing past frames to a fixed context length, so generation cost stays roughly the same for a two-second clip and a minute-long sequence alike. Unlike hosted AI video generators, FramePack runs on your own machine as a desktop app, needs at least 6GB of VRAM, and costs nothing to use.
About Framepack
What Is FramePack
FramePack is a video diffusion model built around a simple idea: stop making the model remember everything. In older video generators, longer clips meant more frames to track, and the memory and compute burden grew with every second you added. FramePack instead packs the input context to a constant length, so processing a huge number of frames doesn't blow up the workload.
That design targets two long-standing problems in AI video. "Forgetting" is when the model loses track of earlier content, so characters or backgrounds change mid-clip. "Drifting" is when small errors pile up and the picture gets worse the longer it runs. FramePack attacks both at once, which is why it can hold visual quality across much longer runs than most local tools. That's the whole point. Longer clips without the usual mess.
The catch is hardware and setup. You need an Nvidia RTX 30XX, 40XX, or 50XX card with at least 6GB of video memory, running Linux or Windows. FramePack ships as a downloadable desktop app you run yourself, not a website you log into. There's no official cloud service or paid tier. It's also a research project at heart, which means rough edges and a command-line-style workflow are normal if you step outside the default app.
Getting Started
- Check your GPU first. You need an Nvidia RTX 30XX, 40XX, or 50XX card with at least 6GB of VRAM; GTX 10XX and 20XX cards aren't tested and may not work.
- Grab the software from the official GitHub repository (github.com/lllyasviel/FramePack) and follow its Windows or Linux install steps. It effectively doubles as the FramePack tutorial, since the repo walks you through setup. Ignore lookalike sites such as framepack.net, framepack.co, or framepack.ai; the project lists them as fake.
- Let it download the pretrained weights on first run, then open the desktop interface.
- Upload a reference image and type a text prompt describing the motion you want, then start generation and adjust length or settings between runs.
- Export the finished clip and feed it back in as the new reference if you want to extend the video further.
Product Information
A quick look at Framepack's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Hobbyists with a gaming PC
- Privacy-conscious creators
- Students and researchers
Tasks
- Turning still images into short animated clips
- Generating long sequences without quality collapse
- Prototyping AI video ideas cheaply
Scenarios
- Weekend creative experiments
- Offline or low-bandwidth work
- Learning AI video from the inside
Key features
Fixed context length for long videos
Long video generation normally gets more expensive as clips get longer, because the model keeps track of more frames. FramePack compresses that history to a constant size, so the compute bottleneck stays flat for a few frames or a few hundred. That's what lets a 13B model run on a laptop-class GPU at all.
Progressive frame compression
Not every frame matters equally to predicting what comes next. FramePack applies heavier compression to less important frames and preserves detail on the ones that count, keeping memory use down while protecting the visual information the model actually needs. Think of it as keeping the important bits sharp and letting the rest blur. That saves memory without costing quality.
Anti-drifting sampling
Drifting is the slow decay that makes longer AI clips look muddy or unstable. FramePack generates frames using context from both directions, which cuts down on error accumulation and keeps picture quality steadier across the length of the video. This matters most for text-to-video runs with no reference image, where there's nothing anchoring the look. Without it, long clips fall apart.
Works with existing models
You don't have to train a video model yourself to use FramePack. It plugs into pretrained video diffusion models and adapts them through fine-tuning, so you can build on work that already exists instead of starting over. Less setup, faster results.
Local desktop app, no cloud needed
Want to skip the cloud entirely? FramePack is delivered as a desktop program with its own sampling and memory management, not a web service. Local video generation keeps your footage private and removes per-clip costs. It also means you handle installation and are responsible for having the right hardware.
Extendable generations
You can take an output clip and use it as the reference for the next round, chaining generations to build a longer video section by section. It's a practical workaround for the fact that no single pass is unlimited. Simple, and it works.
Pros and cons
Pros
- Free and open-source, with no subscription or per-clip pricing.
- Runs on consumer hardware, just 6GB of VRAM and an RTX 30XX or newer card.
- Generation cost stays roughly constant as video length grows, which is rare among AI video tools.
- Everything happens locally, so your prompts and footage stay on your own machine.
- Backed by a published research paper and active code repository you can inspect.
Cons
- Setup is technical: you install from GitHub and download large model weights, which isn't a click-and-go experience.
- No official cloud service or API, so you can't generate video without a capable GPU of your own.
- The project is research-first and there are several lookalike scam sites pretending to be it, so you have to be careful about where you download.
Frequently asked questions
Yes. It's an open-source project released under a free license, and there's no paid tier because there's no official service to pay for. Your only real cost is the hardware you run it on. That's it.
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