From d91dc57d8ab5ea17a60c045da5a7e4f86478fc52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Mon, 20 Mar 2023 20:33:23 +0300 Subject: [PATCH] Update mps.mdx (#2749) Fix typos --- docs/source/en/optimization/mps.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/en/optimization/mps.mdx b/docs/source/en/optimization/mps.mdx index 2dca8372..cada8ce8 100644 --- a/docs/source/en/optimization/mps.mdx +++ b/docs/source/en/optimization/mps.mdx @@ -51,7 +51,7 @@ image = pipe(prompt).images[0] 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. +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. 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. ```python pipeline.enable_attention_slicing()