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)

RuntimeError: CUDA error: No kernel image available for execution on the device

 RuntimeError: CUDA error: No kernel image available for execution on the device. CUDA kernel errors may be reported asynchronously in another API call, so the stack trace below may be incorrect. For debugging, consider passing CUDA_LAUNCH_BLOCKING=1. Compile with TORCH_USE_CUDA_DSA to enable device-side assertions. 

Troubleshooting: "RuntimeError: CUDA error: No kernel image available for execution on the device" 

This error message indicates a problem with your CUDA setup, specifically that the CUDA driver on your system is incompatible with the CUDA toolkit version that PyTorch (or another CUDA-enabled library) was compiled against. It's a common issue, especially after driver updates or when using different versions of CUDA and PyTorch. Here's a breakdown of potential causes and solutions: 

1. CUDA Driver and Toolkit Version Mismatch: 

• Problem: The most frequent cause. Your NVIDIA driver is either too old or too new for the CUDA toolkit version that PyTorch expects. 

• Solution: 

• Check CUDA Version: Determine the CUDA version PyTorch was built with. You can find this information on the PyTorch website (https://pytorch.org/get-started/locally/). Look for the "CUDA" version in the installation command. 

• Check Driver Version: Find your current NVIDIA driver version. 

• Windows: Open NVIDIA Control Panel > System Information. 

• Linux: nvidia-smi (in the terminal) 

• Update/Downgrade Driver: 

• If Driver is Too Old: Update your NVIDIA driver to a version compatible with the CUDA toolkit PyTorch requires. Download the latest driver from the NVIDIA website: https://www.nvidia.com/Download/index.aspx 

• If Driver is Too New: This is trickier. Sometimes, downgrading the driver is necessary. However, this can be complex and may cause issues with other applications. Consider creating a virtual machine with an older driver. 

2. Incorrect PyTorch Installation: 

• Problem: You might have installed a PyTorch version that wasn't compiled with the correct CUDA support. 

• Solution: 

• Reinstall PyTorch: Carefully reinstall PyTorch, ensuring you specify the correct CUDA version during installation. Use the command from the PyTorch website (https://pytorch.org/get-started/locally/). For example: 

 

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

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• (Replace cu118 with the appropriate CUDA version.) 

3. CUDA Toolkit Not Installed or Incorrectly Configured: 

• Problem: The CUDA toolkit itself might not be installed, or it's not properly configured in your system's PATH. 

• Solution: 

• Install CUDA Toolkit: Download and install the CUDA toolkit from the NVIDIA website: https://developer.nvidia.com/cuda-downloads. Make sure to choose the version compatible with your PyTorch installation. 

• Set Environment Variables: Ensure the CUDA toolkit's bin and libnvvp directories are added to your system's PATH environment variable. 

4. Device-Side Assertions (Debugging): 

• Problem: The error message suggests enabling device-side assertions for debugging. 

• Solution: 

• Compile with TORCH_USE_CUDA_DSA: This is more relevant if you're building PyTorch from source. It adds runtime checks to help identify issues. It's unlikely to be the solution if you're using a pre-built PyTorch package. 

5. Blocking Launch (Debugging): 

• Problem: The error message suggests setting CUDA_LAUNCH_BLOCKING=1. 

• Solution: 

• Set Environment Variable: Set the CUDA_LAUNCH_BLOCKING environment variable to 1 before running your code. This forces CUDA to execute kernels synchronously, which can help pinpoint the source of the error. 

• Linux/macOS: export CUDA_LAUNCH_BLOCKING=1 

• Windows: set CUDA_LAUNCH_BLOCKING=1 

6. Insufficient GPU Memory: 

• Problem: Although less common with this specific error message, insufficient GPU memory can sometimes manifest in similar ways. 

• Solution: 

• Reduce Batch Size: If you're training a model, try reducing the batch size. 

• Free GPU Memory: Close other applications that are using the GPU. 

Troubleshooting Steps: 

1. Restart Your Computer: A simple restart can sometimes resolve temporary issues. 

2. Check PyTorch CUDA Availability: In a Python interpreter, run: 

 

import torch
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0))

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• If torch.cuda.is_available() returns False, PyTorch isn't detecting your GPU. 

• Simplify Your Code: Try running a very simple CUDA-enabled PyTorch program to isolate the problem. 

• Search Online: Search for the exact error message and your specific CUDA/PyTorch versions online. 

• To help me provide more specific advice, please tell me: 

• Your operating system: (Windows, Linux, macOS) 

• Your NVIDIA driver version: (e.g., 535.104.05) 

• Your CUDA toolkit version: (if installed) 

• Your PyTorch version: (e.g., 2.0.1) 

• How you installed PyTorch: (pip, conda, from source) 

• The code snippet that's causing the error: (if possible) 

•