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 less than 64 GB of system RAM, or if you generate images at non-standard resolutions larger than 512 × 512 pixels. How to install Python and P圜harm on Apple MacBook Air M1 macOS howtoinstallpython 100 working This video shows in detail a video description of how to install and set up Python on macOS in 2022. 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. Internally, PyTorch uses Apple’s Metal Performance Shaders (MPS) as a backend. PyTorch worked in conjunction with the Metal Engineering team to enable high-performance training on GPU. Prompt = "a photo of an astronaut riding a horse on mars" # First-time "warmup" pass if PyTorch version is 1.13 (see explanation above) How it works PyTorch, like Tensorflow, uses the Metal framework Apple’s Graphics and Compute API. # Recommended if your computer has < 64 GB of RAM Pipe = om_pretrained( "runwayml/stable-diffusion-v1-5") Installation 1) Download the Latest version 2) Mount the File 3) Run P圜harm: This step prompts us to set a UI theme. Copied from diffusers import DiffusionPipeline
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