Hi everyone, I’m currently in the process of converting and optimizing the Stable Diffusion XL model for iOS 18. I followed the steps from the WWDC 2024 session on model optimization, specifically the one titled "Bring your machine learning and AI models to Apple Silicon." I utilized the Stable Diffusion XL model and the tools available in the ml-stable-diffusion GitHub repository and ran the following script to convert the model into an .mlpackage: python3 -m python_coreml_stable_diffusion.torch2coreml \ --convert-unet \ --convert-vae-decoder \ --convert-text-encoder \ --xl-version \ --model-version stabilityai/stable-diffusion-xl-base-1.0 \ --bundle-resources-for-swift-cli \ --refiner-version stabilityai/stable-diffusion-xl-refiner-1.0 \ --attention-implementation SPLIT_EINSUM \ -o ../PotraitModel/ \ --custom-vae-version madebyollin/sdxl-vae-fp16-fix \ --latent-h 128 \ --latent-w 96 \ --chunk-unet The model conversion worked without any issues. However, when I proceeded to optimize the model in a Jupyter notebook, following the same process shown in the WWDC session, I encountered an error during the post-training quantization step. Here’s the code I used for that: op_config = cto_coreml.0pPalettizerConfig( nbits=4, mode="kmeans", granularity="per_grouped_channel", group_size=16, ) config = cto_coreml.OptimizationConfig(op_config) compressed_model = cto_coreml.palettize_weights(mlmodel, config) Unfortunately, I received the following error: AssertionError: The IOS16 only supports per-tensor LUT, but got more than one lut on 0th axis. LUT shape: (80, 1, 1, 1, 16, 1) It appears that the minimum deployment target of the MLModel is set to iOS 16, which might be causing compatibility issues. How can I update the minimum deployment target to iOS 18? If anyone has encountered this issue or knows a workaround, I would greatly appreciate your guidance! Thanks in advance for any help!