FramePack: Efficient Video Generation for Everyone
Here's a quick key takeaway table to grab your attention. It shows the main benefits of FramePack right away.
| Benefit | What It Means |
|---|---|
| Low Memory Use | Works on simple laptops with 6GB GPU. |
| Long Videos | Makes videos up to 60 seconds without quality drop. |
| Easy Setup | No need for complex steps like distillation. |
| Drift Fix | Stops videos from getting blurry over time. |
FramePack is a smart way to make videos using AI. Next, we will explore what it is and why it matters.
What Is FramePack?
FramePack is a research project. It helps create videos with AI models. These models predict the next frame in a video. However, old methods use too much memory. They also lose quality over time. So, FramePack fixes that.
It comes from experts at Stanford University and MIT. They shared it at a big conference called NeurIPS in 2025. In addition, it builds on earlier ideas from 2024. Basically, it makes video making faster and better on regular computers.
How to Use FramePack
First, you need a video AI model with 13 billion parts. For example, use the HY type. Then, encode your starting frames. Use different patch sizes to save memory.
Next, apply FramePack scheduling. This gives more detail to important frames. After that, use anti-drifting sampling. It helps keep quality high. Finally, run it on a GPU like RTX 3060.
It takes about 2.5 seconds per frame. But with tweaks, it drops to 1.5 seconds. So, you can make short or long videos easily.
Core Features
FramePack has key tools that make it stand out. First, Frame Context Packing. This packs frames smartly. It uses less memory by changing patch sizes.
For instance, a big patch means fewer tokens. Tokens are like data bits. So, this keeps things efficient.
Second, Drift Prevention. It uses bi-directional sampling. This means it checks both ways. As a result, videos stay clear.
Third, Flexible Scheduling. You pick which frames get more focus. Therefore, the output matches your needs.
Use Cases
FramePack works well for making long videos. For example, create a 60-second clip from one image.
It also helps with image-to-video tasks. Here, quality stays good over time.
In addition, researchers use it for experiments. They train models on big computers. But you can test on a laptop.
Finally, it suits streaming videos at 30 frames per second.
FAQ
What is Frame Context Packing? It encodes frames with different sizes to save space.
How does anti-drifting work? It samples in two directions to avoid errors.
Can it run on my laptop? Yes, if it has at least 6GB GPU memory.
What models does it support? Mostly 13B HY variants.
Why use inverted sampling? It treats the first frame as a goal for better image-to-video.
Contact Info
https://lllyasviel.github.io/frame_pack_gitpage/ FramePack is a research project, not a company. So, reach out to the creators. They are Lvmin Zhang, Shengqu Cai, Muyang Li, Gordon Wetzstein, and Maneesh Agrawala.
They work at Stanford and MIT. Check the NeurIPS 2025 paper for details. Or look at the arXiv preprint from 2025.
Company Lookup
There is no company behind FramePack. Instead, it is academic work. It started from Stanford and MIT teams.
The project links to a 2024 paper on frame preservation. Thus, it has a short history in AI research.
Technology Stack
FramePack uses video diffusion models. These have 13 billion parameters.
It runs on NVIDIA GPUs like RTX 3060 or 4090. Likely, it uses frameworks like PyTorch.
For videos, it compresses with H.264. Also, it has custom patch kernels for tokens.
Alternatives
Other video models exist. But they need more memory. For example, causal sampling drifts after a few frames.
Some use noise tricks or special guidance. However, these do not fix the core issue.
Rolling timesteps help a bit. Yet, FramePack is better for long videos.
What Are the Most Relevant Questions People Also Ask About This Site
How does FramePack save memory compared to others?
Can I use it for real-time videos?
What patch size works for high-res videos?
How do I add FramePack to my AI model?
Does it handle audio or text inputs?
Where is the source code?
Suggested Best Outline
The outline above works well. But to make it more helpful, start with the table for quick value. Then, add a pros/cons section after features. Also, include a simple diagram idea in use cases. Finally, end with resources for deeper learning. This helps audiences get fast info and stay engaged.