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Easy Set-Up Parallels Desktop automatically detects what you need to get started so you can get up and running within minutes! If you need Windows, you’ll be prompted to download and install Windows 11 or use your Boot Camp installation if you have an Intel-based Mac.Share files and folders, copy and paste images and text, and drag and drop files and content between Mac and Windows applications. Seamless Use Windows, side-by-side, with macOS on your MacBook, MacBook Pro, iMac, iMac Pro, Mac mini or Mac Pro-no restarting required.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. Attention slicing performs the costly attention operation in multiple steps instead of all at once. 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. The system will automatically swap if it needs to, but performance will degrade significantly when it does. M1/M2 performance is very sensitive to memory pressure. Image = pipe(prompt).images Performance Recommendations # Results match those from the CPU device after the warmup pass. Prompt = "a photo of an astronaut riding a horse on mars" # First-time "warmup" pass (see explanation above) # Recommended if your computer has < 64 GB of RAM Pipe = om_pretrained( "runwayml/stable-diffusion-v1-5") You only need to do this pass once, and it’s ok to use just one inference step and discard the result.Ĭopied # make sure you're logged in with `huggingface-cli login` from diffusers import StableDiffusionPipeline

This is a temporary workaround for a weird issue we have detected: the first inference pass produces slightly different results than subsequent ones. We recommend to “prime” the pipeline using an additional one-time pass through it. 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. You can install it with pip or conda using the instructions in. macOS 12.6 or later (13.0 or later recommended).Mac computer with Apple silicon (M1/M2) hardware.These are the steps you need to follow to use your M1 or M2 computer with Stable Diffusion. 🤗 Diffusers is compatible with Apple silicon for Stable Diffusion inference, using the PyTorch mps device. How to use Stable Diffusion in Apple Silicon (M1/M2)