Update README.md

README.md

@@ -17,7 +17,7 @@ They have VAEEncoder.mlmodelc bundles that allow Image2Image to operate correctl
 
 All of the ControlNet models are "Original" ones, built for CPU and GPU compute units (cpuAndGPU) and for SD-1.5 type models.  They will not work with SD-2.1 type models.  The zip files each have a set of models at 4 resolutions.  The 512x512 builds appear to also work with "Split-Einsum" models, using CPU and GPU (cpuAmdGPU), but from my tests, they will not work with "Split-Einsum" models when using the Neural Engine (NE). 
 
-All of the models in this repo work with Swift and the current apple/ml-stable-diffusion pipeline release (0.4.0).  They were not built for, and will not work with, a Python Diffusers pipeline.  They need ml-stable-diffusion (https://github.com/apple/ml-stable-diffusion) for command line use, or a Swift app that supports ControlNet, such as the new (June 2023) Mochi Diffusion 4.0 version (https://github.com/godly-devotion/MochiDiffusion).
+All of the models in this repo work with Swift and the current apple/ml-stable-diffusion pipeline release (0.4.0).  They were not built for, and will not work with, a Python Diffusers pipeline.  They need [**ml-stable-diffusion**](https://github.com/apple/ml-stable-diffusion) for command line use, or a Swift app that supports ControlNet, such as the new (June 2023) [**Mochi Diffusion**](https://github.com/godly-devotion/MochiDiffusion) 4.0 version.
 
 The full SD models are in the "SD" folder of this repo.  They are in subfolders by model name and individually zipped for a particular resolution.  They need to be unzipped for use after downloading.