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Model folders (noted with -cn when built for use with ControlNet) live in the /models folder.

Each -cn model must have a /controlnet folder inside it that is a symlink to the real /controlnet folder.  You need to add this folder symlink to any model you download from my HF repo, after you unzip the model and put it in your /models folder.

(( The SwiftCLI scripts look for the ControlNet *.mlmodelc you are using inside the full model's /controlnet folder.  This is crazy.  It means every full model needs a folder inside symlinked to the /controlnet store folder.  That is how they set it up and I haven't looked into editing their scripts yet. ))

The input image(s) you want to use with the ControlNet need to be inside the /input folder.

Generated images will be saved to the /images folder.

There is a screencap of the folder structure I have that matches all these notes in the MISC section at my HF page.

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Inference Without ControlNet (Using any standard SD-1.5 or SD-2.1 type CoreML model):

Test your setup with this first, before trying with ControlNet.

    conda activate python_playground
    cd xxxxx/miniconda3/envs/python_playground/coreml-swift/ml-stable-diffusion

    swift run StableDiffusionSample "a photo of a cat" --seed 12 --guidance-scale 8.0 --step-count 24 --image-count 1 --scheduler dpmpp --compute-units cpuAndGPU --resource-path ../models/SD21 --output-path ../images

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Inference With ControlNet:

    conda activate python_playground
    cd /Users/jrittvo/miniconda3/envs/python_playground/coreml-swift/ml-stable-diffusion

    swift run StableDiffusionSample "a photo of a green yellow and red bird" --seed 12 --guidance-scale 8.0 --step-count 24 --image-count 1 --scheduler dpmpp --compute-units cpuAndGPU --resource-path ../models/SD15-cn --controlnet Canny --controlnet-inputs ../input/canny-bird.png --output-path ../images


--negative-prompt    "in quotes"
--seed               default is random
--guidance-scale     default is 7
--step-count         default is 50
--image-count        batch size, default is 1
--image              path to image for image2image
--strength           strength for image2image, 0.0 - 1.0, default 0.5
--scheduler          pndm or dpmpp (DPM++), default is pndm
--compute-units      all, cpuOnly, cpuAndGPU, cpuAndNeuralEngine
--resource-path      one of the model checkpoint .mlmodelc bundle folders
--controlnet         path/controlnet-model  <<path/controlnet-model-2>>      (no extension)
--controlnet-inputs  path/image.png  <<path/image-2.png>>      (same order as --controlnet)
--output-path        folder to save image(s)         (auto-named to: prompt.seed.final.png)
--help