InvokeAI/docs/installation/INSTALL_TROUBLESHOOTING.md
2024-03-26 09:18:01 -04:00

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Installation Troubleshooting

!!! info "How to Reinstall"

Many issues can be resolved by re-installing the application. You won't lose any data by re-installing. We suggest downloading the [latest release] and using it to re-install the application.

When you run the installer, you'll have an option to select the version to install. If you aren't ready to upgrade, you choose the current version to fix a broken install.

If the troubleshooting steps on this page don't get you up and running, please either create an issue or hop on discord for help.

OSErrors on Windows while installing dependencies

During a zip file installation or an online update, installation stops with an error like this:

broken-dependency-screenshot{:width="800px"}

To resolve this, re-install the application as described above.

Stable Diffusion XL generation fails after trying to load UNet

InvokeAI is working in other respects, but when trying to generate images with Stable Diffusion XL you get a "Server Error". The text log in the launch window contains this log line above several more lines of error messages:

INFO --> Loading model:D:\LONG\PATH\TO\MODEL, type sdxl:main:unet

This failure mode occurs when there is a network glitch during downloading the very large SDXL model.

To address this, first go to the Model Manager and delete the Stable-Diffusion-XL-base-1.X model. Then, click the HuggingFace tab, paste the Repo ID stabilityai/stable-diffusion-xl-base-1.0 and install the model.

Package dependency conflicts

If you have previously installed InvokeAI or another Stable Diffusion package, the installer may occasionally pick up outdated libraries and either the installer or invoke will fail with complaints about library conflicts.

To resolve this, re-install the application as described above.

InvokeAI runs extremely slowly on Linux or Windows systems

The most frequent cause of this problem is when the installation process installed the CPU-only version of the torch machine-learning library, rather than a version that takes advantage of GPU acceleration. To confirm this issue, look at the InvokeAI startup messages. If you see a message saying ">> Using device CPU", then this is what happened.

To resolve this, re-install the application as described above. Be sure to select the correct GPU device.

Invalid configuration file

Everything seems to install ok, you get a ValidationError when starting up the app.

This is caused by an invalid setting in the invokeai.yaml configuration file. The error message should tell you what is wrong.

Check the configuration docs for more detail about the settings and how to specify them.

Out of Memory Issues

The models are large, VRAM is expensive, and you may find yourself faced with Out of Memory errors when generating images. Here are some tips to reduce the problem:

4 GB of VRAM

This should be adequate for 512x512 pixel images using Stable Diffusion 1.5 and derived models, provided that you do not use the NSFW checker. It won't be loaded unless you go into the UI settings and turn it on.

If you are on a CUDA-enabled GPU, we will automatically use xformers or torch-sdp to reduce VRAM requirements, though you can explicitly configure this. See the configuration docs.

6 GB of VRAM

This is a border case. Using the SD 1.5 series you should be able to generate images up to 640x640 with the NSFW checker enabled, and up to 1024x1024 with it disabled.

If you run into persistent memory issues there are a series of environment variables that you can set before launching InvokeAI that alter how the PyTorch machine learning library manages memory. See https://pytorch.org/docs/stable/notes/cuda.html#memory-management for a list of these tweaks.

12 GB of VRAM

This should be sufficient to generate larger images up to about 1280x1280.