453 lines
23 KiB
Markdown
453 lines
23 KiB
Markdown
# Core ML Suite for ComfyUI
|
||
|
||
## Overview
|
||
|
||
Welcome! In this repository you'll find a set of custom nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
|
||
that allows you to use Core ML models in your ComfyUI workflows.
|
||
These models are designed to leverage the Apple Neural Engine (ANE) on Apple Silicon (M1/M2) machines,
|
||
thereby enhancing your workflows and improving performance.
|
||
|
||
If you're not sure how to obtain these models, you can download them
|
||
[here](https://huggingface.co/coreml-community) or convert your own checkpoints
|
||
directly with the conversion nodes in this suite (see [How to use](#how-to-use)).
|
||
|
||
In simple terms, think of Core ML models as a tool that can help your ComfyUI work faster and more efficiently.
|
||
For instance, during my tests on an M2 Pro 32GB machine,
|
||
the use of Core ML models sped up the generation of 512x512 images by a factor
|
||
of approximately 1.5 to 2 times.
|
||
|
||
## Getting Started
|
||
|
||
To start using custom nodes in your ComfyUI, follow these simple steps:
|
||
|
||
1. Clone or download this repository: You can do this directly into the custom_nodes directory of your ComfyUI.
|
||
2. Install the dependencies: You'll need to use a package manager like pip to do this.
|
||
|
||
That's it! You're now ready to start enhancing your ComfyUI workflows with Core ML models.
|
||
|
||
- Check [Installation](#installation) for more details on installation.
|
||
- Check [How to use](#how-to-use) for more details on how to use the custom nodes.
|
||
- Check [Example Workflows](#example-workflows) for some example workflows.
|
||
|
||
## Glossary
|
||
|
||
- **Core ML**: A machine learning framework developed by Apple. It's used to run machine learning models on Apple
|
||
devices.
|
||
- **Core ML Model**: A machine learning model that can be run on Apple devices using Core ML.
|
||
- **mlmodelc**: A compiled Core ML model. This is the recommended format for Core ML models.
|
||
- **mlpackage**: A Core ML model packaged in a directory. This is the default format for Core ML models.
|
||
- **ANE**: Apple Neural Engine. A hardware accelerator for machine learning tasks on Apple devices.
|
||
- **Compute Unit**: A Core ML option that allows you to specify the hardware on which the model should run.
|
||
- **CPU_AND_ANE**: A Core ML compute unit option that allows the model to run on both the CPU and ANE. This is the
|
||
default option.
|
||
- **CPU_AND_GPU**: A Core ML compute unit option that allows the model to run on both the CPU and GPU.
|
||
- **CPU_ONLY**: A Core ML compute unit option that allows the model to run on the CPU only.
|
||
- **ALL**: A Core ML compute unit option that allows the model to run on all available hardware.
|
||
- **CLIP**: Contrastive Language-Image Pre-training. A model that learns visual concepts from natural language
|
||
supervision. It's used as a text encoder in Stable Diffusion.
|
||
- **VAE**: Variational Autoencoder. A model that learns a latent representation of images. It's used as a prior in
|
||
Stable Diffusion.
|
||
- **Checkpoint**: A file that contains the weights of a model. It's used to load models in Stable Diffusion.
|
||
- **LCM**: [Latent Consistency Model](https://latent-consistency-models.github.io/). A type of model designed to
|
||
generate images with as few steps as possible.
|
||
|
||
> [!NOTE]
|
||
> Note on Compute Units:
|
||
> For the model to run on the ANE, the model must be converted with the `--attention-implementation SPLIT_EINSUM`
|
||
> option.
|
||
> Models converted with `--attention-implementation ORIGINAL` will run on GPU instead of ANE.
|
||
|
||
## Features
|
||
|
||
These custom nodes come with a host of features, including:
|
||
|
||
- Loading Core ML Unet models
|
||
- Support for ControlNet
|
||
- Support for ANE (Apple Neural Engine)
|
||
- Support for CPU and GPU
|
||
- Support for `mlmodelc` and `mlpackage` files
|
||
- Support for SDXL models
|
||
- Support for LCM models
|
||
- Support for LoRAs
|
||
- SD1.5 -> Core ML conversion
|
||
- SDXL -> Core ML conversion
|
||
- LCM -> Core ML conversion
|
||
|
||
> [!NOTE]
|
||
> Please note that using Core ML models can take a bit longer to load initially.
|
||
> For the best experience, I recommend using the compiled models
|
||
> (.mlmodelc files) instead of the .mlpackage files.
|
||
|
||
> [!NOTE]
|
||
> This repository will continue to be updated with more nodes and features over time.
|
||
|
||
## Conversion & Acknowledgements
|
||
|
||
The Core ML conversion pipeline in this repository began as an adaptation of
|
||
Apple's [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion),
|
||
which pioneered running Stable Diffusion on the Apple Neural Engine. The
|
||
implementation has since diverged and no longer depends on that package:
|
||
|
||
- UNet conversion runs natively on `diffusers`' `UNet2DConditionModel`.
|
||
- The ANE-friendly attention path (`SPLIT_EINSUM`, `SPLIT_EINSUM_V2`) is
|
||
reimplemented as standalone `diffusers` attention processors.
|
||
- The toolchain tracks current ComfyUI (NumPy 2, Torch 2.7, coremltools 9,
|
||
Python 3.12).
|
||
|
||
The goal is to keep iterating on these methods independently and to explore
|
||
support beyond SD1.5.
|
||
|
||
> [!IMPORTANT]
|
||
> **Breaking change in 2.0.0.** The converted Core ML UNet now takes
|
||
> `encoder_hidden_states` in the native `diffusers` layout
|
||
> `(batch, tokens, hidden)` instead of the previous
|
||
> `(batch, hidden, 1, tokens)`. Core ML models converted with earlier versions
|
||
> are not compatible with 2.0.0 and must be re-converted.
|
||
|
||
## Installation
|
||
|
||
### Using ComfyUI-Manager
|
||
|
||
The easiest way to install the custom nodes is to use the ComfyUI-Manager. You can find the installation instructions
|
||
[here](https://github.com/ltdrdata/ComfyUI-Manager#installation). Once you've installed the ComfyUI-Manager, you can
|
||
install the custom nodes by following these steps:
|
||
|
||
- Open the ComfyUI-Manager by clicking the `Manager` button in the ComfyUI toolbar.
|
||
- Click the `Install Custom Nodes` button.
|
||
- Search for `Core ML` and click the `Install` button.
|
||
- Restart ComfyUI.
|
||
|
||
### Manual Installation
|
||
|
||
1. Clone this repository into the custom_nodes directory of your ComfyUI. If you're not sure how to do this, you can
|
||
download the repository as a zip file and extract it into the same directory.
|
||
```bash
|
||
cd /path/to/comfyui/custom_nodes
|
||
git clone https://github.com/aszc-dev/ComfyUI-CoreMLSuite.git
|
||
```
|
||
2. Next, install the required dependencies using pip or another package manager:
|
||
|
||
```bash
|
||
cd /path/to/comfyui/custom_nodes/ComfyUI-CoreMLSuite
|
||
pip install -r requirements.txt
|
||
```
|
||
|
||
## How to use
|
||
|
||
Once you've installed the custom nodes, you can start using them in your ComfyUI workflows.
|
||
To do this, you need to add the nodes to your workflow. You can do this by right-clicking on the workflow canvas and
|
||
selecting the nodes from the list of available nodes (the nodes are in the `Core ML Suite` category).
|
||
You can also double-click the canvas and use the search bar to find the nodes. The list of available nodes is given
|
||
below.
|
||
|
||
### Available Nodes
|
||
|
||
#### Core ML UNet Loader (`CoreMLUnetLoader`)
|
||
|
||
|
||
|
||
This node allows you to load a Core ML UNet model and use it in your ComfyUI workflow. Place the converted
|
||
.mlpackage or .mlmodelc file in ComfyUI's `models/unet` directory and use the node to load the model. The output of the
|
||
node is a `coreml_model` object that can be used with the Core ML Sampler.
|
||
|
||
- **Inputs**:
|
||
- **model_name**: The name of the model to load. This should be the name of the .mlpackage or .mlmodelc file.
|
||
- **compute_unit**: The hardware on which the model should run. This can be one of the following:
|
||
- `CPU_AND_ANE`: The model will run on both the CPU and ANE. This is the default option. It works best with
|
||
models
|
||
converted with `--attention-implementation SPLIT_EINSUM` or `--attention-implementation SPLIT_EINSUM_V2`.
|
||
- `CPU_AND_GPU`: The model will run on both the CPU and GPU. It works best with models converted with
|
||
`--attention-implementation ORIGINAL`.
|
||
- `CPU_ONLY`: The model will run on the CPU only.
|
||
- `ALL`: The model will run on all available hardware.
|
||
- **Outputs**:
|
||
- **coreml_model**: A Core ML model that can be used with the Core ML Sampler.
|
||
|
||
#### Core ML Sampler (`CoreMLSampler`)
|
||
|
||
|
||
|
||
This node allows you to generate images using a Core ML model. The node takes a Core ML model as input and outputs a
|
||
latent image similar to the latent image output by the KSampler. This means that you can use the
|
||
resulting latent as you normally would in your workflow.
|
||
|
||
- **Inputs**:
|
||
- **coreml_model**: The Core ML model to use for sampling. This should be the output of the Core ML UNet Loader.
|
||
- **latent_image** [optional]: The latent image to use for sampling. If provided, should be of the same size as the
|
||
input of the Core ML model. If not provided, the node will create a latent suitable for the Core ML model used.
|
||
Useful in img2img workflows.
|
||
- ... _(the rest of the inputs are the same as the KSampler)_
|
||
- **Outputs**:
|
||
- **LATENT**: The latent image output by the Core ML model. This can be decoded using a VAE Decoder or used as input
|
||
to the next node in your workflow.
|
||
|
||
#### Checkpoint Converter
|
||
|
||
|
||
|
||
You can use this node to convert any **SD1.5** based checkpoint to a Core ML model. The converted model is stored in the
|
||
`models/unet` directory and can be used with the `Core ML UNet Loader`. The conversion parameters are encoded in
|
||
the node name, so if the model already exists, the node will not convert it again.
|
||
|
||
- **Inputs**:
|
||
- **ckpt_name**: The name of the checkpoint to convert. This should be the name of the checkpoint file stored in the
|
||
`models/checkpoints` directory.
|
||
- **model_version**: Whether the model is based on SD1.5 or SDXL.
|
||
- **height**: The desired height of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
|
||
- **width**: The desired width of the image generated by the model. The default is 512. Any positive multiple of 8 is accepted.
|
||
- **batch_size**: The batch size of generated images. If you're planning to generate batches of images, you can try
|
||
increasing this value to speed up the generation process. The default is 1.
|
||
- **attention_implementation**: The attention implementation used when converting the model. Choose SPLIT_EINSUM or
|
||
SPLIT_EINSUM_V2 for better ANE support. Choose ORIGINAL for better GPU support.
|
||
- **compute_unit**: The hardware on which the model should run. This is used only when loading the model and doesn't
|
||
affect the conversion process.
|
||
- **controlnet_support**: For the model to support ControlNet, it must be converted with this option set to True.
|
||
The
|
||
default is False.
|
||
- **lora_params** [optional]: Optional LoRA names and weights. If provided, the model will be converted with LoRA(s)
|
||
baked in. More on loading LoRAs below.
|
||
- **Outputs**:
|
||
- **coreml_model**: The converted Core ML model that can be used with Core ML Sampler.
|
||
|
||
> [!NOTE]
|
||
> Some models use a custom config .yaml file. If you're using such a model, you'll need to place the config file in the
|
||
> `models/configs` directory. The config file should be named the same as the checkpoint file. For example, if the
|
||
> checkpoint file is named `juggernaut_aftermath.safetensors`, the config file should be
|
||
> named `juggernaut_aftermath.yaml`.
|
||
> The config file will be automatically loaded during conversion.
|
||
|
||
> [!NOTE]
|
||
> For now, the converter relies heavilty on the model name to determine the conversion parameters. This means that if
|
||
> you change the model name, the node will convert the model again. Other than that, if you find the name too long or
|
||
> confusing, you can change it to anything you want.
|
||
|
||
#### LoRA Loader
|
||
|
||
|
||
|
||
This node allows you to load LoRAs and bake them into a model. Since this is a workaround (as model weights can't be
|
||
modified
|
||
after conversion), there are a few caveats to keep in mind:
|
||
|
||
- The LoRA weights and _strength_model_ parameter are baked into the model. This means that you can't change them
|
||
after conversion. This also means that you need to convert the model again if you want to change the LoRA weights.
|
||
- Loading LoRA affects CLIP, which is not a part of Core ML workflow, so you'll need to load CLIP separately,
|
||
either using `CLIPLoader` or `CheckpointLoaderSimple`. (See [example workflows](#example-workflows) for more details.)
|
||
- After conversion, if you want to load the model using `CoreMLUnetLoader`, you'll need to apply the same LoRAs to
|
||
CLIP manually. (See [example workflows](#example-workflows) for more details.)
|
||
- The LoRA names are encoded in the model name. This means that if you change the name of the LoRA file,
|
||
you'll need to change the model name as well, or the node will convert the model again. (Model strength is not
|
||
encoded, so if you want to change it, you'll need to delete the converted model manually)
|
||
- _strength_clip_ parameter only affects the CLIP model and is not baked into the converted model. This means that
|
||
you can change it after conversion.
|
||
|
||
- **Inputs**:
|
||
- **lora_name**: The name of the LoRA to load.
|
||
- **strength_model**: The strength of the LoRA model.
|
||
- **strength_clip**: The strength of the LoRA CLIP.
|
||
- **lora_params** [optional]: Optional output from other LoRA Loaders.
|
||
- **clip**: The CLIP model to use with the LoRA. This can be either output of the
|
||
`CLIPLoader`/`CheckpointLoaderSimple` or other LoRA Loaders.
|
||
- **Outputs**:
|
||
- **lora_params**: The LoRA parameters that can be passed to the Core ML Converter or other LoRA Loaders.
|
||
- **CLIP**: The CLIP model with LoRA applied.
|
||
|
||
#### LCM Converter
|
||
|
||
|
||
|
||
This node converts [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) model to Core
|
||
ML. The converted model is stored in the `models/unet` directory and can be used with the Core ML UNet Loader. The
|
||
conversion parameteres are encoded in the node name, so if the model already exists, the node will not convert it again.
|
||
|
||
- **Inputs**:
|
||
- **height**: The desired height of the image generated by the model. The default is 512. Must be a multiple of 8.
|
||
- **width**: The desired width of the image generated by the model. The default is 512. Must be a multiple of 8.
|
||
- **batch_size**: The batch size of generated images. If you're planning to generate batches of images, you can try
|
||
increasing this value to speed up the generation process. The default is 1.
|
||
- **compute_unit**: The hardware on which the model should run. This is used only when loading the model and
|
||
doesn't affect the conversion process.
|
||
- **controlnet_support**: For the model to support ControlNet, it must be converted with this option set to True.
|
||
The default is False.
|
||
|
||
> [!NOTE]
|
||
> The conversion process can take a while, so please be patient.
|
||
|
||
> [!NOTE]
|
||
> When using the LCM model with Core ML Sampler, please set _sampler_name_ to `lcm` and _scheduler_ to `sgm_uniform`.
|
||
|
||
#### Core ML Adapter (Experimental) (`CoreMLModelAdapter`)
|
||
|
||
|
||
|
||
This node allows you to use a Core ML as a standard ComfyUI model. This is an experimental node and may not work with
|
||
all models and nodes. Please use with caution and pay attention to the expected inputs of the model.
|
||
|
||
- **Input**:
|
||
- **coreml_model**: The Core ML model to use as a ComfyUI model.
|
||
- **Output**:
|
||
- **MODEL**: The Core ML model wrapped in a ComfyUI model.
|
||
|
||
> [!NOTE]
|
||
> While this approach allows you to use Core ML models with many ComfyUI nodes (both standard and custom), the
|
||
> expected inputs of the model will not be checked, which may cause errors. Please make sure to use a model compatible
|
||
> with the expected parameters.
|
||
|
||
### Example Workflows
|
||
|
||
> [!NOTE]
|
||
> The models used are just an example. Feel free to experiment with different models and see what works best for you.
|
||
|
||
#### Basic txt2img with Core ML UNet loader
|
||
|
||
This is a basic txt2img workflow that uses the Core ML UNet loader to load a model. The CLIP and VAE models
|
||
are loaded using the standard ComfyUI nodes. In the first example, the text encoder (CLIP) and VAE models are loaded
|
||
separately. In the second example, the text encoder and VAE models are loaded from the checkpoint file. Note that you
|
||
can use any CLIP or VAE model as long as it's compatible with Stable Diffusion v1.5.
|
||
|
||
1. **Loading text encoder (CLIP) and VAE models separately**
|
||
- This workflow uses CLIP and VAE models available
|
||
[here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/text_encoder/model.safetensors) and
|
||
[here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.safetensors).
|
||
Once downloaded, place the models in the`models/clip` and `models/vae` directories respectively.
|
||
- The Core ML UNet model is available
|
||
[here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
|
||
Once downloaded, place the model in the `models/unet` directory.
|
||
|
||
2. **Loading text encoder (CLIP) and VAE models from checkpoint file**
|
||
- This workflow loads the CLIP and VAE models from the checkpoint file available
|
||
[here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors).
|
||
Once downloaded, place the model in the`models/checkpoints` directory.
|
||
- The Core ML UNet model is available
|
||
[here](https://huggingface.co/coreml-community/coreml-stable-diffusion-v1-5_cn/blob/main/split_einsum/stable-diffusion-_v1-5_split-einsum_cn.zip).
|
||
Once downloaded, place the model in the `models/unet` directory.
|
||
|
||
|
||
#### ControlNet with Core ML UNet loader
|
||
|
||
This workflow uses the Core ML UNet loader to load a Core ML UNet model that supports ControlNet. The ControlNet is
|
||
being loaded using the standard ComfyUI nodes. Please refer to
|
||
the [basic txt2img workflow](#basic-txt2img-with-core-ml-unet-loader) for more details on how to load the CLIP and VAE
|
||
models.
|
||
The ControlNet model used in this workflow is available
|
||
[here](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/blob/main/diffusion_pytorch_model.fp16.safetensors).
|
||
Once downloaded, place the model in the `models/controlnet` directory.
|
||
|
||
|
||
#### Checkpoint conversion
|
||
|
||
This workflow uses the Checkpoint Converter to convert the checkpoint file. See
|
||
[Checkpoint Converter](#checkpoint-converter) description for more details.
|
||
|
||
|
||
|
||
#### Checkpoint conversion with LoRA
|
||
|
||
This workflow uses the Checkpoint Converter to convert the checkpoint file with LoRA. See
|
||
[LoRA Loader](#lora-loader) description to read more about the caveats of using LoRA.
|
||
|
||
|
||
|
||
#### LCM LoRA conversion
|
||
|
||
Please note that you can use multiple LoRAs with the same model. To do this, you'll need to use multiple LoRA Loaders.
|
||
> [!IMPORTANT]
|
||
> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
|
||
> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
|
||
|
||
|
||
|
||
#### Loader with LoRAs
|
||
|
||
This workflow uses the Core ML UNet Loader to load a model with LoRAs. The CLIP must be loaded separately and passed
|
||
through the same LoRA nodes as during conversion. See [LoRA Loader](#lora-loader) description to read more about the
|
||
caveats of using LoRA. Since _lora_name_ and _strength_model_ are baked into the model, it is not necessary to pass
|
||
them as inputs to the loader.
|
||
> [!IMPORTANT]
|
||
> In this example, the model is passed through the adapter and `ModelSamplingDiscrete` nodes to a standard ComfyUI's
|
||
> KSampler (not Core ML Sampler). ModelSamplingDiscrete needs to be used to sample models with LCM LoRAs properly.
|
||
|
||
|
||
|
||
#### LCM conversion with ControlNet
|
||
|
||
This workflow uses LCM converter to
|
||
convert [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
|
||
model to Core ML. The converted model can then be used with or without ControlNet to generate images.
|
||
|
||
|
||
#### SDXL Base + Refiner conversion
|
||
|
||
This is a basic workflow for SDXL. You add LoRAs and ControlNets the same way as in the previous examples.
|
||
You can also skip the refiner step.
|
||
|
||
The models used in this workflow are available at the following links:
|
||
|
||
- [Base model + text_encoder (clip) + text_encoder_2 (clip2)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
|
||
- [Refiner model](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
|
||
- [VAE](https://huggingface.co/stabilityai/sdxl-vae)
|
||
|
||
> [!IMPORTANT]
|
||
> SDXL on ANE is not supported. If loading of the model gets stuck, please try using CPU_AND_GPU or CPU_ONLY.
|
||
> For best results, use ORIGINAL attention implementation.
|
||
|
||
|
||
|
||
## Quantization (opt-in)
|
||
|
||
The `Core ML Converter` and `Core ML LCM Converter` nodes accept an
|
||
optional `quantize_nbits` dropdown that runs k-means weight palettization
|
||
(`coremltools.optimize.coreml.palettize_weights`) on the UNet before save.
|
||
|
||
Values: `none` (default — no quantization, identical to unquantized
|
||
behavior and filenames), `8`, `6`, `4`. The number is appended to the
|
||
.mlpackage stem as `_q<bits>` so quantized and unquantized variants
|
||
coexist on disk and in cache.
|
||
|
||
### SD1.5 1×512×512 SPLIT_EINSUM tradeoffs (M2 Pro, ANE)
|
||
|
||
Measured with 20 UNet forward passes at a fixed seed for the PSNR
|
||
comparison:
|
||
|
||
| nbits | size (MB) | size vs none | fwd median (ms) | PSNR vs `none` (dB) |
|
||
|---|---:|---:|---:|---:|
|
||
| none | 1641 | 1.000 | 197.1 | — |
|
||
| 8 | 822 | 0.501 | 186.6 | 53.5 |
|
||
| 6 | 617 | 0.376 | 183.0 | 40.2 |
|
||
| 4 | 412 | 0.251 | 179.8 | 27.5 |
|
||
|
||
PSNR here is computed on the raw `noise_pred` output of a single UNet
|
||
forward at a fixed seed, not on the final decoded image — it isolates
|
||
the quantization-induced drift from sampler / VAE noise. Final-image
|
||
PSNR is comfortably higher (the sampler averages over 20 steps).
|
||
|
||
### Recommended settings per chip / RAM
|
||
|
||
- **8 GB RAM (M1 base, M2 base):** `nbits=4`. ~4× smaller model, still
|
||
loads, PSNR 27 dB is visually identical at SD1.5 sizes.
|
||
- **16 GB RAM (M1/M2/M3 Pro):** `nbits=6` is the sweet spot — ~2.7×
|
||
smaller, PSNR 40 dB, no perceptible quality drop.
|
||
- **32 GB+ RAM (Max / Ultra):** `nbits=8` if you want the safety
|
||
margin, `none` if you want bit-identical output for golden testing.
|
||
|
||
The default stays `none` so existing workflows produce byte-for-byte
|
||
identical output.
|
||
|
||
## Limitations
|
||
|
||
- Core ML models are fixed in terms of their inputs and outputs.
|
||
This means you'll need to use latent images of the same size as the input of the model (512x512 is the default for
|
||
SD1.5).
|
||
However, you can re-convert the model to a different input size using the
|
||
conversion nodes in this suite (set the desired width and height).
|
||
- SD2.1 models are not supported.
|
||
|
||
[^1]:
|
||
Unless [EnumeratedShapes](https://apple.github.io/coremltools/docs-guides/source/flexible-inputs.html#select-from-predetermined-shapes)
|
||
is used during conversion. Needs more testing.
|
||
|
||
## Support
|
||
|
||
I'm here to help! If you have any questions or suggestions, don't hesitate to open an issue and I'll do my best
|
||
to assist you.
|