#
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 12.6 or later (13.0 or later recommended).
- arm64 version of Python.
- PyTorch 1.13. You can install it with `pip` or `conda` using the instructions in https://pytorch.org/get-started/locally/.

##
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("runwayml/stable-diffusion-v1-5")
    pipe = pipe.to("mps")

    # Recommended if your computer has < 64 GB of RAM
    pipe.enable_attention_slicing()

    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]

##
Performance Recommendations

M1/M2 performance is very sensitive to memory pressure. The system will automatically swap if it needs to, but performance will degrade significantly when it does.

We recommend you use attention slicing to reduce memory pressure during inference and prevent swapping, particularly if your computer has lass than 64 GB of system RAM, or if you generate images at non-standard resolutions larger than 512 × 512 pixels. Attention slicing performs the costly attention operation in multiple steps instead of all at once. It usually has a performance impact of ~20% in computers without universal memory, but we have observed better performance in most Apple Silicon computers, unless you have 64 GB or more.

    pipeline.enable_attention_slicing()

##
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 is related to the `mps` backend in PyTorch. This is being resolved, but for now we recommend to iterate instead of batching.