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](https://github.com/invoke-ai/InvokeAI/releases/latest) and using it to re-install the application. Consult the [installer guide](../installation/010_INSTALL_AUTOMATED.md) for more information.
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.
Note that any releases marked as _pre-release_ are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the [latest release].
The Model Manager tab in the UI provides a few ways to install models, including using your already-downloaded models. You'll see a popup directing you there on first startup. For more information, see the [model install docs].
- Check the [system requirements] to ensure that your system is capable of generating images.
- Check the `ram` setting in `invokeai.yaml`. This setting tells Invoke how much of your system RAM can be used to cache models. Having this too high or too low can slow things down. That said, it's generally safest to not set this at all and instead let Invoke manage it.
- Check the `vram` setting in `invokeai.yaml`. This setting tells Invoke how much of your GPU VRAM can be used to cache models. Counter-intuitively, if this setting is too high, Invoke will need to do a lot of shuffling of models as it juggles the VRAM cache and the currently-loaded model. The default value of 0.25 is generally works well for GPUs without 16GB or more VRAM. Even on a 24GB card, the default works well.
- Check that your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup. If your GPU isn't used, re-install to ensure the correct versions of torch get installed.
Ensure that you checked **Add python.exe to PATH** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, you can re-run the python installer, choose the Modify option and check the box.
## Triton error on startup
This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
## Updated to 3.4.0 and xformers can’t load C++/CUDA
An issue occurred with your PyTorch update. Follow these steps to fix :
1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
- If you run into an error with `typing_extensions`, re-open the developer console and run: `pip install -U typing-extensions`
Note that v3.4.0 is an old, unsupported version. Please upgrade to the [latest release].
## Install failed and says `pip` is out of date
An out of date `pip` typically won't cause an installation to fail. The cause of the error can likely be found above the message that says `pip` is out of date.
If you saw that warning but the install went well, don't worry about it (but you can update `pip` afterwards if you'd like).
Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
## 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 during installation or update
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.
## 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:
!!! info "Optimizing for GPU VRAM"
=== "4GB VRAM GPU"
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].
=== "6GB VRAM GPU"
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.
=== "12GB VRAM GPU"
This should be sufficient to generate larger images up to about 1280x1280.
## Memory Leak (Linux)
If you notice a memory leak, it could be caused to memory fragmentation as models are loaded and/or moved from CPU to GPU.
A workaround is to tune memory allocation with an environment variable:
```bash
# Force blocks >1MB to be allocated with `mmap` so that they are released to the system immediately when they are freed.
MALLOC_MMAP_THRESHOLD_=1048576
```
!!! warning "Speed vs Memory Tradeoff"
Your generations may be slower overall when setting this environment variable.
!!! info "Possibly dependent on `libc` implementation"
It's not known if this issue occurs with other `libc` implementations such as `musl`.
If you encounter this issue and your system uses a different implementation, please try this environment variable and let us know if it fixes the issue.
<h3>Detailed Discussion</h3>
Python (and PyTorch) relies on the memory allocator from the C Standard Library (`libc`). On linux, with the GNU C Standard Library implementation (`glibc`), our memory access patterns have been observed to cause severe memory fragmentation.
This fragmentation results in large amounts of memory that has been freed but can't be released back to the OS. Loading models from disk and moving them between CPU/CUDA seem to be the operations that contribute most to the fragmentation.
This memory fragmentation issue can result in OOM crashes during frequent model switching, even if `ram` (the max RAM cache size) is set to a reasonable value (e.g. a OOM crash with `ram=16` on a system with 32GB of RAM).
This problem may also exist on other OSes, and other `libc` implementations. But, at the time of writing, it has only been investigated on linux with `glibc`.
To better understand how the `glibc` memory allocator works, see these references:
Note the differences between memory allocated as chunks in an arena vs. memory allocated with `mmap`. Under `glibc`'s default configuration, most model tensors get allocated as chunks in an arena making them vulnerable to the problem of fragmentation.
