Preparing a working environment with Pytorch should be straightforward nowadays, but it’s not as simple when dealing with ARM64 chips on Apple Devices. I admit I took some shortcuts and didn’t use Docker Images (which goes against what I advocate at work) because I couldn’t find reliable image sources for Kohya and Automatic1111 with Apple Silicon backends.

In any case, even though I did not use containers, I utilized virtual environments to keep libraries as isolated as possible. At the time of writing this post, I’m using a brand-new MacBook Pro, so I didn’t want to tinker with the built-in Python. Instead, I followed these guidelines on how to set it up correctly.

Environment Preparation


Kohya’s GUI provides a graphical user interface for Kohya’s Stable Diffusion trainers scripts.

To prepare the environment, follow these steps:

  • Create a virtualenv with Python 3.10.6. It seems that using other Python versions might lead to problems, so it’s best to stick with that.
# Clone kohya project
git clone
cd ./kohya_ss
# Create environment within the folder
virtualenv -p python3.10 venv
# Activate the environment
source venv/bin/activate
# pytorch
pip install torch torchvision
# tensorflow
pip install tensorflow-macos tensorflow-metal tensorboard
# diffusersのインストール
pip install diffusers
# requirements.txt for kohya
pip install -r requirements.txt
  • Configure accelerator settings
# Run accelerate config for entering a configuration process
accelerate config
In which compute environment are you running?
This machine                                                                    
Which type of machine are you using?                                            
No distributed training                                                         
Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:NO                                                   
Do you wish to optimize your script with torch dynamo?[yes/NO]:NO               
Do you wish to use FP16 or BF16 (mixed precision)?
accelerate configuration saved at ~/.cache/huggingface/accelerate/default_config.yaml

We’re almost all set up. We still need to make a few changes in some lines of code to use MPS instead of CUDA. To do that, you have to replace cuda with mps, as explained at the end of the post we’ve already reviewed.


Installing Automatic1111 was a breeze. I followed the instructions for Installation on Apple Silicon included in the repository.