InvokeAI/docs/installation/INSTALL_MAC.md

---
title: macOS
---

# :fontawesome-brands-apple: macOS

Invoke AI runs quite well on M1 Macs and we have a number of M1 users
in the community.

While the repo does run on Intel Macs, we only have a couple
reports. If you have an Intel Mac and run into issues, please create
an issue on Github and we will do our best to help.

## Requirements

- macOS 12.3 Monterey or later
- About 10GB of storage (and 10GB of data if your internet connection has data caps)
- Any M1 Macs or an Intel Macs with 4GB+ of VRAM (ideally more)

## Installation

First you need to download a large checkpoint file.

1. Sign up at https://huggingface.co
2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
3. Accept the terms and click Access Repository
4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder). You may want to move it somewhere else for longer term storage - SD needs this file to run.

While that is downloading, open Terminal and run the following commands one at a time, reading the comments and taking care to run the appropriate command for your Mac's architecture (Intel or M1).

!!! todo "Homebrew"

    If you have no brew installation yet (otherwise skip):

    ```bash title="install brew (and Xcode command line tools)"
    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
    ```

!!! todo "Conda Installation"

    Now there are two different ways to set up the Python (miniconda) environment:

       1. Standalone
       2. with pyenv

    If you don't know what we are talking about, choose Standalone. If you are familiar with python environments, choose "with pyenv"

    === "Standalone"

        ```bash title="Install cmake, protobuf, and rust"
        brew install cmake protobuf rust
        ```

        Then choose the kind of your Mac and install miniconda:

        === "M1 arm64"

            ```bash title="Install miniconda for M1 arm64"
            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh \
              -o Miniconda3-latest-MacOSX-arm64.sh
            /bin/bash Miniconda3-latest-MacOSX-arm64.sh
            ```

        === "Intel x86_64"

            ```bash title="Install miniconda for Intel"
            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh \
              -o Miniconda3-latest-MacOSX-x86_64.sh
            /bin/bash Miniconda3-latest-MacOSX-x86_64.sh
            ```

    === "with pyenv"

        ```bash
        brew install pyenv-virtualenv
        pyenv install anaconda3-2022.05
        pyenv virtualenv anaconda3-2022.05
        eval "$(pyenv init -)"
        pyenv activate anaconda3-2022.05
        ```

!!! todo "Clone the Invoke AI repo"

    ```bash 
    git clone https://github.com/invoke-ai/InvokeAI.git
    cd InvokeAI
    ```

!!! todo "Wait until the checkpoint-file download finished, then proceed"

    We will leave the big checkpoint wherever you stashed it for long-term storage,
    and make a link to it from the repo's folder. This allows you to use it for
    other repos, or if you need to delete Invoke AI, you won't have to download it again.

    ```{.bash .annotate}
    # Make the directory in the repo for the symlink
    mkdir -p models/ldm/stable-diffusion-v1/

    # This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
    PATH_TO_CKPT="$HOME/Downloads" # (1)!

    # Create a link to the checkpoint
    ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
    ```

    1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)

!!! todo "Create the environment & install packages"

    === "M1 Mac"

        ```bash
        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
        ```

    === "Intel x86_64 Mac"

        ```bash
        PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
        ```

    ```bash
    # Activate the environment (you need to do this every time you want to run SD)
    conda activate invokeai

    # This will download some bits and pieces and make take a while
    (invokeai) python scripts/preload_models.py

    # Run SD!
    (invokeai) python scripts/dream.py

    # or run the web interface!
    (invokeai) python scripts/invoke.py --web

    # The original scripts should work as well.
    (invokeai) python scripts/orig_scripts/txt2img.py \
        --prompt "a photograph of an astronaut riding a horse" \
        --plms
    ```
    !!! info

        `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
        create -f environment-mac.yml` never finishing in some situations. So
        it isn't required but wont hurt.
---

## Common problems

After you followed all the instructions and try to run invoke.py, you might
get several errors. Here's the errors I've seen and found solutions for.

### Is it slow?

```bash title="Be sure to specify 1 sample and 1 iteration."
python ./scripts/orig_scripts/txt2img.py \
  --prompt "ocean" \
  --ddim_steps 5 \
  --n_samples 1 \
  --n_iter 1
```

---

### Doesn't work anymore?

PyTorch nightly includes support for MPS. Because of this, this setup
is inherently unstable. One morning I woke up and it no longer worked
no matter what I did until I switched to miniforge. However, I have
another Mac that works just fine with Anaconda. If you can't get it to
work, please search a little first because many of the errors will get
posted and solved. If you can't find a solution please [create an
issue](https://github.com/invoke-ai/InvokeAI/issues).

One debugging step is to update to the latest version of PyTorch nightly.

```bash
conda install \
  pytorch \
  torchvision \
  -c pytorch-nightly \
  -n ldm
```

If it takes forever to run `conda env create -f environment-mac.yml`, try this:

```bash
git clean -f
conda clean \
  --yes \
  --all
```

Or you could try to completley reset Anaconda:

```bash
conda update \
  --force-reinstall \
  -y \
  -n base \
  -c defaults conda
```

---

### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc

There are several causes of these errors:

1. Did you remember to `conda activate ldm`? If your terminal prompt begins with
   "(invokeai)" then you activated it. If it begins with "(base)" or something else
   you haven't.

2. You might've run `./scripts/preload_models.py` or `./scripts/invoke.py`
   instead of `python ./scripts/preload_models.py` or
   `python ./scripts/invoke.py`. The cause of this error is long so it's below.

    <!-- I could not find out where the error is, otherwise would have marked it as a footnote -->

3. if it says you're missing taming you need to rebuild your virtual
   environment.

    ```bash
    conda deactivate
    conda env remove -n ldm
    conda env create -f environment-mac.yml
    ```

4. If you have activated the ldm virtual environment and tried rebuilding it,
   maybe the problem could be that I have something installed that you don't and
   you'll just need to manually install it. Make sure you activate the virtual
   environment so it installs there instead of globally.

    ```bash
    conda activate ldm
    pip install <package name>
    ```

You might also need to install Rust (I mention this again below).

---

### How many snakes are living in your computer?

You might have multiple Python installations on your system, in which case it's
important to be explicit and consistent about which one to use for a given
project. This is because virtual environments are coupled to the Python that
created it (and all the associated 'system-level' modules).

When you run `python` or `python3`, your shell searches the colon-delimited
locations in the `PATH` environment variable (`echo $PATH` to see that list) in
that order - first match wins. You can ask for the location of the first
`python3` found in your `PATH` with the `which` command like this:

```bash
% which python3
/usr/bin/python3
```

Anything in `/usr/bin` is
[part of the OS](https://developer.apple.com/library/archive/documentation/FileManagement/Conceptual/FileSystemProgrammingGuide/FileSystemOverview/FileSystemOverview.html#//apple_ref/doc/uid/TP40010672-CH2-SW6).
However, `/usr/bin/python3` is not actually python3, but rather a stub that
offers to install Xcode (which includes python 3). If you have Xcode installed
already, `/usr/bin/python3` will execute
`/Library/Developer/CommandLineTools/usr/bin/python3` or
`/Applications/Xcode.app/Contents/Developer/usr/bin/python3` (depending on which
Xcode you've selected with `xcode-select`).

Note that `/usr/bin/python` is an entirely different python - specifically,
python 2. Note: starting in macOS 12.3, `/usr/bin/python` no longer exists.

```bash
% which python3
/opt/homebrew/bin/python3
```

If you installed python3 with Homebrew and you've modified your path to search
for Homebrew binaries before system ones, you'll see the above path.

```bash
% which python
/opt/anaconda3/bin/python
```

If you have Anaconda installed, you will see the above path. There is a
`/opt/anaconda3/bin/python3` also.

We expect that `/opt/anaconda3/bin/python` and `/opt/anaconda3/bin/python3`
should actually be the _same python_, which you can verify by comparing the
output of `python3 -V` and `python -V`.

```bash
(invokeai) % which python
/Users/name/miniforge3/envs/ldm/bin/python
```

The above is what you'll see if you have miniforge and correctly activated the
ldm environment, while usingd the standalone setup instructions above.

If you otherwise installed via pyenv, you will get this result:

```bash
(anaconda3-2022.05) % which python
/Users/name/.pyenv/shims/python
```

It's all a mess and you should know
[how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
if you want to fix it. Here's a brief hint of the most common ways you can
modify it (don't really have the time to explain it all here).

- ~/.zshrc
- ~/.bash_profile
- ~/.bashrc
- /etc/paths.d
- /etc/path

Which one you use will depend on what you have installed, except putting a file
in /etc/paths.d - which also is the way I prefer to do.

Finally, to answer the question posed by this section's title, it may help to
list all of the `python` / `python3` things found in `$PATH` instead of just the
first hit. To do so, add the `-a` switch to `which`:

```bash
% which -a python3
...
```

This will show a list of all binaries which are actually available in your PATH.

---

### Debugging?

Tired of waiting for your renders to finish before you can see if it works?
Reduce the steps! The image quality will be horrible but at least you'll get
quick feedback.

```bash
python ./scripts/txt2img.py \
  --prompt "ocean" \
  --ddim_steps 5 \
  --n_samples 1 \
  --n_iter 1
```

---

### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'

```bash
python scripts/preload_models.py
```

---

### "The operator [name] is not current implemented for the MPS device." (sic)

!!! example "example error"

    ```bash
    ... NotImplementedError: The operator 'aten::_index_put_impl_' is not current
    implemented for the MPS device. If you want this op to be added in priority
    during the prototype phase of this feature, please comment on
    https://github.com/pytorch/pytorch/issues/77764.
    As a temporary fix, you can set the environment variable
    `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op.
    WARNING: this will be slower than running natively on MPS.
    ```

The InvokeAI version includes this fix in
[environment-mac.yml](https://github.com/invoke-ai/InvokeAI/blob/main/environment-mac.yml).

### "Could not build wheels for tokenizers"

I have not seen this error because I had Rust installed on my computer before I
started playing with Stable Diffusion. The fix is to install Rust.

```bash
curl \
  --proto '=https' \
  --tlsv1.2 \
  -sSf https://sh.rustup.rs | sh
```

---

### How come `--seed` doesn't work?

First this:

> Completely reproducible results are not guaranteed across PyTorch releases,
> individual commits, or different platforms. Furthermore, results may not be
> reproducible between CPU and GPU executions, even when using identical seeds.

[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)

Second, we might have a fix that at least gets a consistent seed sort of. We're
still working on it.

### libiomp5.dylib error?

```bash
OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
```

You are likely using an Intel package by mistake. Be sure to run conda with the
environment variable `CONDA_SUBDIR=osx-arm64`, like so:

`CONDA_SUBDIR=osx-arm64 conda install ...`

This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in
by a dependency.
[nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
is a metapackage designed to prevent this, by making it impossible to install
`mkl`, but if your environment is already broken it may not work.

Do _not_ use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
masks the underlying issue of using Intel packages.

---

### Not enough memory

This seems to be a common problem and is probably the underlying problem for a
lot of symptoms (listed below). The fix is to lower your image size or to add
`model.half()` right after the model is loaded. I should probably test it out.
I've read that the reason this fixes problems is because it converts the model
from 32-bit to 16-bit and that leaves more RAM for other things. I have no idea
how that would affect the quality of the images though.

See [this issue](https://github.com/CompVis/stable-diffusion/issues/71).

---

### "Error: product of dimension sizes > 2\*\*31'"

This error happens with img2img, which I haven't played with too much yet. But I
know it's because your image is too big or the resolution isn't a multiple of
32x32. Because the stable-diffusion model was trained on images that were 512 x
512, it's always best to use that output size (which is the default). However,
if you're using that size and you get the above error, try 256 x 256 or 512 x
256 or something as the source image.

BTW, 2\*\*31-1 =
[2,147,483,647](https://en.wikipedia.org/wiki/2,147,483,647#In_computing), which
is also 32-bit signed [LONG_MAX](https://en.wikipedia.org/wiki/C_data_types) in
C.

---

### I just got Rickrolled! Do I have a virus?

You don't have a virus. It's part of the project. Here's
[Rick](https://github.com/invoke-ai/InvokeAI/blob/main/assets/rick.jpeg)
and here's [the
code](https://github.com/invoke-ai/InvokeAI/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
that swaps him in. It's a NSFW filter, which IMO, doesn't work very
good (and we call this "computer vision", sheesh).

---

### My images come out black

We might have this fixed, we are still testing.

There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
on CUDA GPU's where the images come out green. Maybe it's the same issue?
Someone in that issue says to use "--precision full", but this fork actually
disables that flag. I don't know why, someone else provided that code and I
don't know what it does. Maybe the `model.half()` suggestion above would fix
this issue too. I should probably test it.

### "view size is not compatible with input tensor's size and stride"

```bash
File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```

Update to the latest version of invoke-ai/InvokeAI. We were
patching pytorch but we found a file in stable-diffusion that we could
change instead. This is a 32-bit vs 16-bit problem.

### The processor must support the Intel bla bla bla

What? Intel? On an Apple Silicon?

```bash
Intel MKL FATAL ERROR: This system does not meet the minimum requirements for use of the Intel(R) Math Kernel Library. The processor must support the Intel(R) Supplemental Streaming SIMD Extensions 3 (Intel(R) SSSE3) instructions. The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions. The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
```

This is due to the Intel `mkl` package getting picked up when you try to install
something that depends on it-- Rosetta can translate some Intel instructions but
not the specialized ones here. To avoid this, make sure to use the environment
variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
use ARM packages, and use `nomkl` as described above.

---

### input types 'tensor<2x1280xf32>' and 'tensor<\*xf16>' are not broadcast compatible

May appear when just starting to generate, e.g.:

```bash
invoke> clouds
Generating:   0%|                                                              | 0/1 [00:00<?, ?it/s]/Users/[...]/dev/stable-diffusion/ldm/modules/embedding_manager.py:152: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1662016319283/work/aten/src/ATen/mps/MPSFallback.mm:11.)
  placeholder_idx = torch.where(
                                                                                                    loc("mps_add"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/20d6c351-ee94-11ec-bcaf-7247572f23b4/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":219:0)): error: input types 'tensor<2x1280xf32>' and 'tensor<*xf16>' are not broadcast compatible
LLVM ERROR: Failed to infer result type(s).
Abort trap: 6
/Users/[...]/opt/anaconda3/envs/ldm/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
```