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FramePack

App : A product you use directly

Local long video generation with only 6 GB of VRAM.

The most accessible local video generation tool.

Official site GitHub · 17,320 ★

Overall score

7.3 /10

Security: nothing to flag

Utility

8.0

Reliability

2.0

Security

No caveat on record

Ease

5 min · Comfortable

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Generating long videos locally is usually reserved for machines equipped with high-end GPUs with large amounts of video memory. FramePack solves this problem by compressing the input context to a constant length, making the computational workload independent of video duration: even a one-minute video requires no more resources than a short one.

In practice, the tool works in a frame-by-frame or section-by-section prediction mode. You load a starting image, write a descriptive prompt, and FramePack generates the video progressively. You can see frames appear as they are generated, without waiting for the entire process to complete.

FramePack is aimed at people who want to generate videos from an image and a text prompt, on their own computer, without a subscription or sending data to a remote server. You need an Nvidia GPU from the RTX 30XX, 40XX, or 50XX series with at least 6 GB of video memory, running Windows or Linux.

However, the tool is not suitable for users without a compatible Nvidia GPU, or for those looking for a plug-and-play solution without technical installation. GTX 10XX and 20XX GPUs are not supported, and the initial model download exceeds 30 GB.

What it offers

·Video generation up to 60 seconds at 30 frames per second from an image and a descriptive text prompt.
·Fully local operation: no data is sent to an external server.
·Real-time preview of generated frames, section by section, during generation.
·TeaCache option to speed up generation by approximately 30%, at the cost of a slight quality loss depending on hardware.
·Graphical interface accessible via a web browser, based on Gradio.
·Compatibility with several attention engines to optimize performance: PyTorch, xformers, flash-attn, sage-attention.
·Support for the FramePack-F1 variant, available alongside the main version.
·One-click installation on Windows via a preconfigured package (CUDA 12.6 + PyTorch 2.6).

Our tips

Use TeaCache to quickly test your ideas and settings, then disable it for the final generation to achieve the best possible quality.
On Windows, always run update.bat before using run.bat to ensure you have the latest version and avoid known bugs.
If the generated video appears very short in the interface (around 1 second), do not stop the process: this is the normal behavior of the section-by-section prediction mode, the video grows progressively.
Before testing your own images, run the sanity check provided in the documentation using the sample images and prompts, to confirm your hardware is working correctly.
The same caution that applies to TeaCache also applies to sage-attention, bnb quantization, and gguf formats: prefer them for exploration, not for final production.

Watch out

TeaCache can noticeably degrade visual quality depending on the hardware used: approximately 70% of users get a result different from the reference, sometimes worse.
The official GitHub repository is the only legitimate download point. Many third-party sites impersonate the FramePack name and may be dangerous: do not download anything or pay on those sites.

In practice

Price: Free
Commercial use: Commercial use: yes
Available on: windows, linux
Licence: Apache-2.0
Runs locally: Yes, 6 GB VRAM recommended
FR interface: No

Security and data

Rating: Security: nothing to flag
Your data: Data stays local
Maintenance: Inactive for over six months
Last release: 18 April 2025

Links and repository checked on 25 October 2026 · added on 1 April 2025

How to install FramePack

1. Check that your Nvidia GPU (RTX 30XX, 40XX, or 50XX) has at least 6 GB of video memory. 2. Download only from the official GitHub repository (github.com/lllyasviel/FramePack): beware of any other website. 3. On Windows: download the One-Click package (framepack_cu126_torch26.7z) and extract it. 4. On Windows: run update.bat to update, then run.bat to launch the interface. 5. On Linux: install Python 3.10, then run pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 and pip install -r requirements.txt, then launch python demo_gradio.py. 6. Models (over 30 GB) are downloaded automatically from HuggingFace on first launch.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 && pip install -r requirements.txt

In the same category

Whole category Video →