FramePack

FramePack

Official implementation and desktop software for "Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models".

Links: Paper, Project Page

FramePack is a next-frame (next-frame-section) prediction neural network structure that generates videos progressively. 

FramePack compresses input contexts to a constant length so that the generation workload is invariant to video length.

FramePack can process a very large number of frames with 13B models even on laptop GPUs.

FramePack can be trained with a much larger batch size, similar to the batch size for image diffusion training.

Video diffusion, but feels like image diffusion.

News

2025 July 14: Some pure text2video anti-drifting stress-test results of FramePack-P1 are uploaded here, using common prompts without any reference images.

2025 June 26: Some results of FramePack-P1 are uploaded here. The FramePack-P1 will be the next version of FramePack with two designs: Planned Anti-Drifting and History Discretization.

2025 May 03: The FramePack-F1 is released. Try it here.

Note that this GitHub repository is the only official FramePack website. We do not have any web services. All other websites are spam and fake, including but not limited to framepack.co, frame_pack.co, framepack.net, frame_pack.net, framepack.ai, frame_pack.ai, framepack.pro, frame_pack.pro, framepack.cc, frame_pack.cc,framepackai.co, frame_pack_ai.co, framepackai.net, frame_pack_ai.net, framepackai.pro, frame_pack_ai.pro, framepackai.cc, frame_pack_ai.cc, and so on. Again, they are all spam and fake. Do not pay money or download files from any of those websites.

Requirements

Note that this repo is a functional desktop software with minimal standalone high-quality sampling system and memory management.

Start with this repo before you try anything else!

Requirements:

* Nvidia GPU in RTX 30XX, 40XX, 50XX series that supports fp16 and bf16. The GTX 10XX/20XX are not tested.
* Linux or Windows operating system.
* At least 6GB GPU memory.

To generate 1-minute video (60 seconds) at 30fps (1800 frames) using 13B model, the minimal required GPU memory is 6GB. (Yes 6 GB, not a typo. Laptop GPUs are okay.)

About speed, on my RTX 4090 desktop it generates at a speed of 2.5 seconds/frame (unoptimized) or 1.5 seconds/frame (teacache). On my laptops like 3070ti laptop or 3060 laptop, it is about 4x to 8x slower. Troubleshoot if your speed is much slower than this.

In any case, you will directly see the generated frames since it is next-frame(-section) prediction. So you will get lots of visual feedback before the entire video is generated.

Installation

Windows:

>>> Click Here to Download One-Click Package (CUDA 12.6 + Pytorch 2.6) 

Copy this prompt:

The man dances energetically, leaping mid-air with fluid arm swings and quick footwork.

Set like this:

(all default parameters, with teacache turned off)

The result will be:

      Video may be compressed by GitHub

Important Note:

Again, this is a next-frame-section prediction model. This means you will generate videos frame-by-frame or section-by-section.

If you get a much shorter video in the UI, like a video with only 1 second,

……（后略，见原文）