--- 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 `. #### **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//stable-diffusion/tree/` 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//stable-diffusion/tree/` 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.