issue with the latest hardware (RTX 5070) and the evolving landscape of Stable Diffusion web UIs and xFormers

You're running into a very common issue with the latest hardware (RTX 5070) and the evolving landscape of Stable Diffusion web UIs and xFormers. Here's a detailed breakdown of why you're seeing the "TypeError: 'NoneType' object is not iterable" error with xFormers in the reForge web UI (based on illyasviel's Forge) and CUDA 12.8, and how to fix it.

The Core Problem: CUDA 12.8 and xFormers Compatibility (and reForge's Specifics)

• CUDA 12.8 is Very New: CUDA 12.8 is a very recent release. xFormers, while actively developed, often lags behind the very latest CUDA versions. The core issue is that the xFormers build you're likely using hasn't been compiled specifically to support CUDA 12.8 yet.
• reForge's Build Process: reForge (based on illyasviel's Forge) has a specific way it handles dependencies and builds. It's often more sensitive to these kinds of compatibility issues than some other web UIs.
• xFormers Initialization Failure: The "NoneType" error indicates that xFormers is failing to initialize correctly within the reForge environment when CUDA 12.8 is present. It's likely a function within xFormers is expecting a list or iterable, but it's receiving None because the initialization process didn't complete successfully.

Solutions (in order of likelihood of success):

Force a Rebuild of xFormers with CUDA 12.8 Support (Most Likely Solution):

• This is the most effective approach. You need to explicitly tell the system to rebuild xFormers against your CUDA 12.8 installation. Here's how:
• Navigate to the reForge Directory: Open a command prompt or terminal and navigate to the root directory of your reForge installation.
• Run the Build Command: Execute the following command:

pip install xformers --index-url https://download.pytorch.org/whl/cu121

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Important: Even though you have CUDA 12.8, use the cu121 index URL. This is a known workaround for getting xFormers to build correctly with newer CUDA versions. The developers are working on full CUDA 12.8 support, but this is the current best practice.

 

• Restart reForge: After the installation completes, restart the reForge web UI.

Downgrade CUDA (Less Ideal, but a Workaround):

• If rebuilding xFormers doesn't work, you could temporarily downgrade your CUDA toolkit to a version that xFormers officially supports (e.g., CUDA 11.8). This is less ideal because you're losing the benefits of the latest CUDA features.
• Uninstall CUDA 12.8: Completely uninstall CUDA 12.8 from your system.
• Install CUDA 11.8: Install CUDA 11.8.
• Reinstall PyTorch: Reinstall PyTorch with CUDA 11.8 support.
• Install xFormers: Install xFormers.
• Restart reForge: Restart the reForge web UI.

Disable xFormers (Temporary Solution):

• As a temporary workaround, you can disable xFormers by removing the --xformers argument when launching the reForge web UI. This will allow you to generate images, but you'll lose the performance and memory benefits of xFormers.

Check reForge's GitHub Issues:

• Visit the reForge GitHub repository (https://github.com/illlyasviel/reForge) and search for existing issues related to CUDA 12.8 and xFormers. Someone else might have already found a solution or reported the problem to the developers.

Important Considerations:

• Clean Installation: If you've tried multiple installations and downgrades, it's a good idea to start with a clean installation of reForge and CUDA to avoid conflicts.
• Driver Compatibility: Ensure your NVIDIA driver is compatible with CUDA 12.8. Update to the latest driver if necessary.
• Monitor reForge Updates: The reForge developers are actively working on improving compatibility with the latest CUDA versions. Keep an eye on the GitHub repository for updates and fixes.

To help me further, could you confirm:

• The exact command you're using to launch reForge: (including all arguments)
• The output of nvidia-smi: (This will show your NVIDIA driver version and CUDA version)
• The output of python -c "import torch; print(torch.version.cuda)": (This will confirm the CUDA version PyTorch is using)