Step 1: Install Conda

To start, download the Anaconda installer from the following link:

After downloading, verify the integrity of the file using this command:

hasum -a 256 ~/Downloads/Anaconda3-2022.10-MacOSX-arm64.sh

You should see a hash that matches:

200700077db8eed762fbc996b830c3f8cc5a2bb7d6b20bb367147eb35f2dcc72

Next, execute the installation script with the command:

bash Anaconda3–2022.10-MacOSX-arm64.sh

Follow the prompts to complete the installation.

Step 2: Install Python

For this setup, we need Python version 3.7, 3.8, or 3.9. We will use 3.8. Create a new Conda environment with the command:

conda create -n coreml_stable_diffusion python=3.8 -y

Then, activate your new environment:

conda activate coreml_stable_diffusion

Resolving Tokenizers Error on macOS

A common issue that arises is the Tokenizers error. You can fix this by installing Rust:

Verify the installation with:

rustc --version

Step 3: Initialize the Stable Diffusion Project

Clone the Stable Diffusion repository with:

Navigate to the repository directory and install the required dependencies:

cd ml-stable-diffusion

pip install -r requirements.txt

Verifying File Integrity After Conversion

You can verify the model conversion with the following command:

python -m python_coreml_stable_diffusion.pipeline --prompt "magic book on the table" -i ./models -o ./output --compute-unit ALL --seed 93

This command performs several tasks: it adds the models, saves the output, allows CPU/GPU usage, and uses a seeded random variable for reproducibility. The prompt used here is "a magic book on a table".

After running the command, it may take a few minutes to generate the image.

Step 4: Simplifying Image Generation with a Web UI

To enhance the image generation experience, we will create a web interface using Gradio, a Python library for building web applications.

Create a file named web.py with the following content:

import python_coreml_stable_diffusion.pipeline as pipeline

import gradio as gr

from diffusers import StableDiffusionPipeline

def init(args):

pipeline.logger.info("Initializing PyTorch pipe for reference configuration")

pytorch_pipe = StableDiffusionPipeline.from_pretrained(args.model_version, use_auth_token=True)

coreml_pipe = pipeline.get_coreml_pipe(pytorch_pipe=pytorch_pipe, mlpackages_dir=args.i, model_version=args.model_version, compute_unit=args.compute_unit)

def infer(prompt, steps):

pipeline.logger.info("Beginning image generation.")

image = coreml_pipe(prompt=prompt, height=coreml_pipe.height, width=coreml_pipe.width, num_inference_steps=steps)

return [image["images"][0]]

demo = gr.Blocks()

with demo:

gr.Markdown("<center><h1>Core ML Stable Diffusion</h1>Run Stable Diffusion on Apple Silicon with Core ML</center>")

with gr.Group():

with gr.Box():

with gr.Row():

with gr.Column():

text = gr.Textbox(label="Prompt", lines=11, placeholder="Enter your prompt")

btn = gr.Button("Generate image")

steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=10, step=1)

with gr.Column():

gallery = gr.Gallery(label="Generated image", elem_id="gallery")

text.submit(infer, inputs=[text, steps], outputs=gallery)

btn.click(infer, inputs=[text, steps], outputs=gallery)

demo.launch(debug=True, server_name="0.0.0.0")

if __name__ == "__main__":

parser = pipeline.argparse.ArgumentParser()

parser.add_argument("-i", required=True, help=("Path to input directory with the .mlpackage files generated by python_coreml_stable_diffusion.torch2coreml"))

parser.add_argument("--model-version", default="CompVis/stable-diffusion-v1-4", help=("The pre-trained model checkpoint and configuration to restore."))

parser.add_argument("--compute-unit", choices=pipeline.get_available_compute_units(), default="ALL", help=("The compute units to be used when executing Core ML models."))

args = parser.parse_args()

init(args)

Save this file in the python_coreml_stable_diffusion directory, and run the following command:

python -m python_coreml_stable_diffusion.web -i ./models --compute-unit ALL

You should see logs indicating that the web service is running. Access it via http://0.0.0.0:7860.

Testing the Web Interface

In your browser, input "colorful star trails" as the prompt and click "Generate image." Wait for the image to appear on the right side.

Now, generating images has become much more straightforward. You only need to modify the prompt text and click a single button to produce your image, which saves you the hassle of adjusting command parameters or navigating through files.

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This video tutorial covers the basics of running Stable Diffusion on a Mac, providing a visual guide to the setup process.

In this video, learn how to efficiently run Stable Diffusion on an Apple Silicon Mac with straightforward steps.