#
Metal Performance Shaders (MPS)

🤗 Diffusers is compatible with Apple silicon (M1/M2 chips) using the PyTorch `mps` device, which uses the Metal framework to leverage the GPU on MacOS devices. You’ll need to have:

- macOS computer with Apple silicon (M1/M2) hardware
- macOS 12.6 or later (13.0 or later recommended)
- arm64 version of Python
- PyTorch 2.0 (recommended) or 1.13 (minimum version supported for `mps`)

The `mps` backend uses PyTorch’s `.to()` interface to move the Stable Diffusion pipeline on to your M1 or M2 device:

    from diffusers import DiffusionPipeline

    pipe = DiffusionPipeline.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"
    image = pipe(prompt).images[0]
    image

Generating multiple prompts in a batch can crash or fail to work reliably. We believe this is related to the `mps` backend in PyTorch. While this is being investigated, you should iterate instead of batching.

If you’re using PyTorch 1.13, you need to “prime” the pipeline with an additional one-time pass through it. This is a temporary workaround for an issue where the first inference pass produces slightly different results than subsequent ones. You only need to do this pass once, and after just one inference step you can discard the result.

      from diffusers import DiffusionPipeline

      pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("mps")
      pipe.enable_attention_slicing()

      prompt = "a photo of an astronaut riding a horse on mars"
      # First-time "warmup" pass if PyTorch version is 1.13
    + _ = pipe(prompt, num_inference_steps=1)

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

##
Troubleshoot

M1/M2 performance is very sensitive to memory pressure. When this occurs, the system automatically swaps if it needs to which significantly degrades performance.

To prevent this from happening, we recommend attention slicing to reduce memory pressure during inference and prevent swapping. This is especially relevant if your computer has less than 64GB of system RAM, or if you generate images at non-standard resolutions larger than 512×512 pixels. Call the enable_attention_slicing() function on your pipeline:

    from diffusers import DiffusionPipeline
    import torch

    pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to("mps")
    pipeline.enable_attention_slicing()

Attention slicing performs the costly attention operation in multiple steps instead of all at once. It usually improves performance by ~20% in computers without universal memory, but we’ve observed better performance in most Apple silicon computers unless you have 64GB of RAM or more.