ComfyUI Node: Convert Checkpoint to Core ML

Convert Checkpoint to Core ML:

The Core ML Converter node is designed to transform a Latent Consistency Model (LCM) into a Core ML model, making it compatible with Apple's machine learning framework. This conversion process allows you to leverage the power of Core ML for efficient and optimized model execution on Apple devices, including iPhones, iPads, and Macs. By converting your models to Core ML, you can take advantage of hardware acceleration and other performance enhancements provided by Apple's ecosystem. This node simplifies the conversion process, ensuring that your models are ready for deployment in a Core ML environment with minimal effort.

Convert Checkpoint to Core ML Input Parameters:

ckpt_name

This parameter specifies the name of the checkpoint file that contains the model you wish to convert. It is essential for identifying the correct model file from your directory. The checkpoint file should be located in the designated folder for checkpoints.

model_version

This parameter defines the version of the model you are converting. You can choose between SD15 and SDXL, which correspond to different versions of the Stable Diffusion model. Selecting the correct version ensures that the conversion process uses the appropriate configurations and optimizations.

height

This parameter sets the height of the target image for the model. It accepts integer values with a default of 512, a minimum of 256, and a maximum of 2048, in steps of 8. The height value impacts the resolution of the generated images and should be chosen based on your specific requirements.

width

This parameter sets the width of the target image for the model. Similar to the height parameter, it accepts integer values with a default of 512, a minimum of 256, and a maximum of 2048, in steps of 8. The width value also affects the resolution of the generated images.

batch_size

This parameter determines the batch size for the model during conversion. It accepts integer values with a default of 1, a minimum of 1, and a maximum of 64. The batch size influences the number of images processed simultaneously and can impact the performance and memory usage of the model.

attention_implementation

This parameter specifies the type of attention implementation to use during the conversion. You can choose from SPLIT_EINSUM, SPLIT_EINSUM_V2, and ORIGINAL. Each option represents a different method of handling attention mechanisms within the model, which can affect the model's performance and accuracy.

compute_unit

This parameter defines the compute unit to use when loading the model. Options include CPU_AND_NE, CPU_AND_GPU, ALL, and CPU_ONLY. Selecting the appropriate compute unit ensures that the model utilizes the available hardware resources efficiently, optimizing performance for your specific device.

controlnet_support

This boolean parameter indicates whether ControlNet support should be enabled during the conversion. The default value is False. Enabling ControlNet support allows the model to incorporate additional control mechanisms, which can enhance its capabilities and flexibility.

lora_params

This optional parameter allows you to specify LoRA (Low-Rank Adaptation) parameters for the model. It accepts a dictionary of LoRA parameter names and their corresponding values. These parameters can be used to fine-tune the model's performance and adapt it to specific tasks or datasets.

Convert Checkpoint to Core ML Output Parameters:

COREML_UNET

The output of the Core ML Converter node is a COREML_UNET model. This converted Core ML model is optimized for execution on Apple devices, providing efficient and accelerated performance. The COREML_UNET model can be used for various tasks, including image generation and manipulation, leveraging the capabilities of the Core ML framework.

Convert Checkpoint to Core ML Usage Tips:

• Ensure that the checkpoint file specified in the ckpt_name parameter is correctly located in the designated folder to avoid file not found errors.
• Choose the appropriate model_version based on the specific requirements of your task to ensure optimal performance and compatibility.
• Adjust the height and width parameters to match the desired resolution of the generated images, keeping in mind the impact on performance and memory usage.
• Select the compute_unit that best matches your device's hardware capabilities to maximize the efficiency of the converted model.
• Enable controlnet_support if your application requires additional control mechanisms, but be aware of the potential impact on performance.

Convert Checkpoint to Core ML Common Errors and Solutions:

FileNotFoundError: Checkpoint file not found

• Explanation: The specified checkpoint file in the ckpt_name parameter could not be located in the designated folder.
• Solution: Verify that the checkpoint file exists in the correct directory and that the ckpt_name parameter is correctly specified.

ValueError: Invalid model version

• Explanation: The model_version parameter contains an invalid value that is not recognized by the converter.
• Solution: Ensure that the model_version parameter is set to either SD15 or SDXL, as these are the supported versions.

ValueError: Height/Width out of range

• Explanation: The values specified for the height or width parameters are outside the allowed range.
• Solution: Adjust the height and width parameters to be within the specified range (256 to 2048) and ensure they are multiples of 8.

RuntimeError: Unsupported compute unit

• Explanation: The compute_unit parameter is set to a value that is not supported by the device.
• Solution: Select a valid compute unit from the available options (CPU_AND_NE, CPU_AND_GPU, ALL, CPU_ONLY) that matches your device's capabilities.

TypeError: Invalid LoRA parameters

• Explanation: The lora_params parameter contains invalid or incorrectly formatted values.
• Solution: Ensure that the lora_params parameter is a dictionary with valid LoRA parameter names and values, and that the values are correctly formatted.