How to use Stable Diffusion in Apple Silicon (M1/M2)

🤗 Diffusers is compatible with Apple silicon for Stable Diffusion inference, using the PyTorch mps device. These are the steps you need to follow to use your M1 or M2 computer with Stable Diffusion.

Requirements

• Mac computer with Apple silicon (M1/M2) hardware.
• macOS 12.3 or later.
• arm64 version of Python.
• PyTorch Preview (Nightly), version 1.14.0.dev20221007 or later.

Inference Pipeline

The snippet below demonstrates how to use the mps backend using the familiar to() interface to move the Stable Diffusion pipeline to your M1 or M2 device.

We recommend to “prime” the pipeline using an additional one-time pass through it. This is a temporary workaround for a weird issue we have detected: the first inference pass produces slightly different results than subsequent ones. You only need to do this pass once, and it’s ok to use just one inference step and discard the result.

# make sure you're logged in with `huggingface-cli login`
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipe = pipe.to("mps")

prompt = "a photo of an astronaut riding a horse on mars"

# First-time "warmup" pass (see explanation above)
_ = pipe(prompt, num_inference_steps=1)

# Results match those from the CPU device after the warmup pass.
image = pipe(prompt).images[0]

Known Issues

• As mentioned above, we are investigating a strange first-time inference issue.
• Generating multiple prompts in a batch crashes or doesn’t work reliably. We believe this might be related to the mps backend in PyTorch, but we need to investigate in more depth. For now, we recommend to iterate instead of batching.

Performance

These are the results we got on a M1 Max MacBook Pro with 64 GB of RAM, running macOS Ventura Version 13.0 Beta (22A5331f). We performed Stable Diffusion text-to-image generation of the same prompt for 50 inference steps, using a guidance scale of 7.5.