---
title: F.A.Q.
---

# :material-frequently-asked-questions: F.A.Q.

## **Frequently-Asked-Questions**

Here are a few common installation problems and their solutions. Often these are
caused by incomplete installations or crashes during the install process.

---

### During `conda env create`, conda hangs indefinitely

If it is because of the last PIP step (usually stuck in the Git Clone step, you
can check the detailed log by this method):

```bash
export PIP_LOG="/tmp/pip_log.txt"
touch ${PIP_LOG}
tail -f ${PIP_LOG} &
conda env create -f environment-mac.yaml --debug --verbose
killall tail
rm ${PIP_LOG}
```

**SOLUTION**

Conda sometimes gets stuck at the last PIP step, in which several git
repositories are cloned and built.

Enter the stable-diffusion directory and completely remove the `src` directory
and all its contents. The safest way to do this is to enter the stable-diffusion
directory and give the command `git clean -f`. If this still doesn't fix the
problem, try "conda clean -all" and then restart at the `conda env create` step.

To further understand the problem to checking the install lot using this method:

```bash
export PIP_LOG="/tmp/pip_log.txt"
touch ${PIP_LOG}
tail -f ${PIP_LOG} &
conda env create -f environment-mac.yaml --debug --verbose
killall tail
rm ${PIP_LOG}
```

---

### `invoke.py` crashes with the complaint that it can't find `ldm.simplet2i.py`

Or it complains that function is being passed incorrect parameters.

**SOLUTION**

Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory
and give the command `pip install -e .`

---

### Missing modules

`invoke.py` dies, complaining of various missing modules, none of which starts
with `ldm`.

**SOLUTION**

From within the `InvokeAI` directory, run `conda env update` This is also
frequently the solution to complaints about an unknown function in a module.

---

### How can I try new features

There's a feature or bugfix in the Stable Diffusion GitHub that you want to try
out.

**SOLUTIONS**

#### **Main Branch**

If the fix/feature is on the `main` branch, enter the stable-diffusion directory
and do a `git pull`.

Usually this will be sufficient, but if you start to see errors about missing or
incorrect modules, use the command `pip install -e .` and/or `conda env update`
(These commands won't break anything.)

`pip install -e .` and/or `conda env update -f environment.yaml`

(These commands won't break anything.)

#### **Sub Branch**

If the feature/fix is on a branch (e.g. "_foo-bugfix_"), the recipe is similar,
but do a `git pull <name of branch>`.

#### **Not Committed**

If the feature/fix is in a pull request that has not yet been made part of the
main branch or a feature/bugfix branch, then from the page for the desired pull
request, look for the line at the top that reads "_xxxx wants to merge xx
commits into lstein:main from YYYYYY_". Copy the URL in YYYY. It should have the
format

`https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`

Then **go to the directory above stable-diffusion** and rename the directory to
"_stable-diffusion.lstein_", "_stable-diffusion.old_", or anything else. You can
then git clone the branch that contains the pull request:

`git clone https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`

You will need to go through the install procedure again, but it should be fast
because all the dependencies are already loaded.

---

### CUDA out of memory

Image generation crashed with CUDA out of memory error after successful
sampling.

**SOLUTION**

Try to run script with option `--free_gpu_mem` This will free memory before
image decoding step.
