How to Upgrade FramePack Studio for NVIDIA 50-Series GPUs

November 15, 2025 amida168 Machine Learning 0

FramePack Studio now includes native support for NVIDIA’s 50-series GPUs, but many early adopters installed the application long before Blackwell cards were available. If you simply drop a new 50-series GPU into that older setup, FramePack Studio won’t automatically switch to the updated runtime or CUDA stack it needs to use the new architecture. As a result, users may see slow performance, missing compute capabilities, or the app failing to launch.
This guide explains how to upgrade an existing FramePack Studio installation on Windows so it correctly detects and accelerates on a 50-series card—without reinstalling the entire application. The process involves updating specific backend components and replacing outdated libraries that shipped with earlier builds.

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Step-by-Step Guide: Upgrading an Existing FramePack Studio Installation for 50-Series GPU Support

This guide shows how to update an older FramePack Studio installation on Windows so it can correctly use an NVIDIA 50-series GPU. These steps assume you already have a virtual environment (venv) set up for FramePack Studio, and you are using Python 3.10 and CUDA 12.8.

1. (Optional) Save Your Current Environment

Activate your virtual environment and export the current package list:

venv\Scripts\activate
pip freeze > before-upgrade-requirements.txt

2. Activate the Virtual Environment

Run this in Command Prompt:

venv\Scripts\activate

If using PowerShell:

venv\Scripts\Activate.ps1

3. Check Installed Packages

pip list

4. Uninstall Existing PyTorch Packages

pip uninstall -y torch torchaudio torchvision

5. Install the Updated PyTorch Build

If using the official CUDA 12.8 index:

pip install torch==2.8.0 torchaudio torchvision --index-url https://download.pytorch.org/whl/cu128

If you have downloaded wheel files locally, install them like this:

pip install C:\path\to\torch-2.8.0+cu128-cp310-cp310-win_amd64.whl
pip install C:\path\to\torchaudio-*.whl
pip install C:\path\to\torchvision-*.whl

Make sure the wheel filenames match your Python version and architecture.

6. Uninstall SageAttention

pip uninstall -y sageattention

7. Install the Updated SageAttention Wheel

Replace the path with the exact wheel you downloaded:

pip install "C:\Users\username\Downloads\sageattention-2.1.1+cu128torch2.8.0-cp310-cp310-win_amd64.whl"

8. Uninstall Flash Attention

pip uninstall -y flash_attn

9. Install the Updated Flash Attention Wheel

Replace the path with the exact wheel you downloaded:
pip install "C:\Users\username\Downloads\flash_attn-2.7.4+cu128torch2.8-cp310-cp310-win_amd64.whl"

10. Verify the Upgrade

Run these commands to confirm everything works:

pip list
python -c "import torch; print(torch.__version__, torch.cuda.is_available())"
python -c "import sageattention; print('sageattention OK')"
python -c "import flash_attn; print('flash_attn OK')"

You should see:

  • Torch version: 2.8.0

  • CUDA available: True

11. Restart FramePack Studio

Close the program completely and reopen it so the new components load correctly.

Troubleshooting Tips

CUDA Not Available:
Update your NVIDIA driver to the latest version. 50-series GPUs require new driver support.

Wheel Installation Fails:
Ensure the wheel filenames match Python 3.10 (cp310) and Windows (win_amd64).

DLL Load Errors:
Use the correct CUDA-enabled wheel builds. Most official PyTorch wheels for Windows already bundle the CUDA runtime.

Dependency Conflicts:
Run:

pip check

This will list packages that need to be resolved.

Conclusion

After updating the required runtime files and replacing the older CUDA and backend libraries, an existing FramePack Studio installation will recognize the 50-series GPU and run using its full Blackwell acceleration path. Users who upgraded their hardware without reinstalling the app will immediately see faster generation times, better stability, and improved memory handling with FP and F1 models.
If you originally installed FramePack Studio before 50-series support was added, performing this manual upgrade is the easiest way to unlock the performance your new GPU is capable of—without starting from scratch.

Further Reading

How to Install Triton and SageAttention on Windows

Install Triton & SageAttention on Windows (RTX 50 Series)

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