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, 4.0, 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 or ml-stable-diffusion 1.0.0, 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 new (June 2023) [**Mochi Diffusion**](https://github.com/godly-devotion/MochiDiffusion) 4.0 version.
+All of the models in this repo work with Swift and the apple/ml-stable-diffusion pipeline (release 0.4.0 or 1.0.0).  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.
 
@@ -25,11 +25,11 @@ The ControlNet model files are in the "CN" folder of this repo.  They are zipped
 
 There is also a "MISC" folder that has text files with some notes and a screencap of my directory structure.  These are provided for those who want to convert models themselves and/or run the models with a SwiftCLI.  The notes are not perfect, and may be out of date if any of the Python or CoreML packages referenced have been updated recently.  You can open a Discussion here if you need help with any of the "MISC" items. 
 
-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.
+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.  Briefly, they will need to go inside a "controlnet" folder that you placed inside your base model folder.  You'll need a "controlnet" folder inside each base model folder, or a symlink named "controlnet" pointing to a central folder with all your ControlNet models inside it.
 
 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.
 
-Please note that when you unzip the ControlNet files (for example Canny.zip) from this repo, they will unzip into a folder, with the actual model files inside that folder.  This folder is just for the zipping process.  **What you want to move into your ControlNet model folder in Mochi Diffusion will be the individual files, not the folder they unzip into.**  This is different from base models where you do want to copy the folder itself.  See the images [**here**](https://huggingface.co/jrrjrr/CoreML-Models-For-ControlNet/blob/main/CN/-Settings.jpg) and [**here**](https://huggingface.co/jrrjrr/CoreML-Models-For-ControlNet/blob/main/CN/-Folders.jpg) for an example of how my folders are set up.
+Please note that when you unzip the ControlNet files (for example Canny.zip) from this repo, they will unzip into a folder, with the actual model files inside that folder.  This folder is just for the zipping process.  **What you want to move into your ControlNet model folder in Mochi Diffusion will be the individual files, not the folder they unzip into.**  This is different from base models where you do want to copy the folder itself.  See the images [**here**](https://huggingface.co/jrrjrr/CoreML-Models-For-ControlNet/blob/main/CN/-Settings.jpg) and [**here**](https://huggingface.co/jrrjrr/CoreML-Models-For-ControlNet/blob/main/CN/-Folders.jpg) for an example of how my folders are set up for Mochi Diffusion.
 
 The sizes noted for all model type inputs/outputs are WIDTH x HEIGHT.  A 512x768 is "portrait" orientation and a 768x512 is "landscape" orientation.