lllyasviel / lllyasviel/stable-diffusion-webui-forge

5070 Ti not supported? ForgeUI fails with TypeError and CUDA kernel errors

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Python
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Description

Hi ForgeUI team,

I recently upgraded my GPU from a Zotac RTX 3060 to a Zotac RTX 5070 Ti Amp Extreme (Blackwell architecture) and have been trying to get ForgeUI working on it for the past couple of days with no success.

Here are the issues I’m facing:

I followed the instructions to install Python 3.10.x, created a virtual environment, installed Forge UI, and installed the correct nightly version of PyTorch with CUDA 12.8 support.

I also made sure CUDA 12.8 is installed on my system.

I’ve reinstalled Python, ForgeUI, Torch/Torchvision, InsightFace, etc. multiple times.

When launching ForgeUI, I get this error consistently:

TypeError: 'NoneType' object is not iterable

CUDA error: no kernel image is available for execution on the device

I’m reaching out here after exhausting Reddit, ChatGPT, and every workaround I could find. Can someone please confirm if 50-series (Blackwell) GPUs like the 5070 Ti are currently supported? If not, is there a patch or dev branch being worked on?

Any help or even acknowledgment would mean a lot — I’m happy to test things or share logs if needed.

System Info:

  • GPU: Zotac RTX 5070 Ti Amp Extreme 16GB

  • CUDA: 12.8

  • Python: 3.10.6

  • PyTorch: 2.5.1+cu128 (nightly)

  • OS: Windows 10

Thanks in advance!

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reproducing ForgeUI on the listed Windows 10, Python 3.10.6, PyTorch 2.5.1+cu128, CUDA 12.8, and RTX 5070 Ti setup. Examine the launch output for the reported TypeError and CUDA kernel error; the work is done when Blackwell support is confirmed with a tested resolution or the unsupported configuration is clearly documented.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
ai, machine-learning
Issue type
Bug
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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