Update README.md

README.md

@@ -13,11 +13,11 @@ The SD models are all "Original" (not "Split-Einsum") and built for CPU and GPU.
 
 The Stable Diffusion v1.5 model and the other SD 1.5 type models contain both the standard Unet and the ControlledUnet used for a ControlNet pipeline.  The correct one will be used automatically based on whether a ControlNet is enabled or not.
 
-They have VAEEncoder.mlmodelc bundles that allow Image2Image to operate correctly at the noted resolutions, when used with a current Swift CLI pipeline or a current GUI built with ml-stable-diffusion 0.4.0, such as Mochi Diffusion 3.2 or later.
+They have VAEEncoder.mlmodelc bundles that allow Image2Image to operate correctly at the noted resolutions, when used with a current Swift CLI pipeline or a current GUI built with ml-stable-diffusion 0.4.0, such as Mochi Diffusion 3.2, 4.0, or later.
 
 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 Mochi Diffusion (https://github.com/godly-devotion/MochiDiffusion) test version currently in a closed beta test.  Join the Mochi Difusion Discord server (https://discord.gg/x2kartzxGv) to request access to the beta test version.
+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).
 
 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.
 
@@ -27,11 +27,11 @@ There is also a "MISC" folder that has text files with some notes and a screenca
 
 For command line use, the "MISC" notes cover setting up a miniconda3 environment.  If you are using the command line, please read the notes concerning naming and placement of your ControlNet model folder.
 
-If you are using a GUI, that app will most likely guide you to the correct location/arrangement for your ConrolNet model folder.
+If you are using a GUI like Mochi Diffusion 4.0, the app will most likely guide you to the correct location/arrangement for your ConrolNet model folder.
 
 The sizes noted for all model type inputs/outputs are WIDTH x HEIGHT.  A 512x768 is "portrait" orientation and a 768x512 is "landscape" orientation.
 
-**If you encounter any models that do not work correctly with image2image and/or a ControlNet, using the current apple/ml-stable-diffusion SwiftCLI pipeline for i2i or CN, or Mochi Diffusion 3.2 using i2i, or the Mochi Diffusion beta test build using i2i or CN, please leave a report in the Community Discussion area.  If you would like to add models that you have converted, leave a message there as well, and I'll grant you access to this repo.**
+**If you encounter any models that do not work correctly with image2image and/or a ControlNet, using the current apple/ml-stable-diffusion SwiftCLI pipeline for i2i or CN, or Mochi Diffusion 3.2 using i2i, or Mochi Diffusion 4.0 using i2i or CN, please leave a report in the Community Discussion area.  If you would like to add models that you have converted, leave a message there as well, and I'll grant you access to this repo.**
  
 ## Base Models - A Variety Of SD-1.5-Type Models For Use With ControlNet
 Each folder contains 4 zipped model files, output sizes as indicated: 512x512, 512x768, 768x512 or 768x768