Core ML Stable Diffusion

Run Stable Diffusion on Apple Silicon with Core ML

This repository comprises:

python_coreml_stable_diffusion, a Python package for converting PyTorch models to Core ML format and performing image generation with Hugging Face diffusers in Python
StableDiffusion, a Swift package that developers can add to their Xcode projects as a dependency to deploy image generation capabilities in their apps. The Swift package relies on the Core ML model files generated by python_coreml_stable_diffusion

If you run into issues during installation or runtime, please refer to the FAQ section. Please refer to the System Requirements section before getting started.

System Requirements

Model Conversion:

macOS	Python	coremltools
13.1	3.8	7.0

Project Build:

macOS	Xcode	Swift
13.1	14.3	5.8

Target Device Runtime:

macOS	iPadOS, iOS
13.1	16.2

Target Device Runtime (With Memory Improvements):

macOS	iPadOS, iOS
14.0	17.0

Target Device Hardware Generation:

Mac	iPad	iPhone
M1	M1	A14

Performance Benchmarks

stabilityai/stable-diffusion-2-1-base Benchmark:

Device	`--compute-unit`	`--attention-implementation`	End-to-End Latency (s)	Diffusion Speed (iter/s)
iPhone 12 Mini	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	18.5	1.44
iPhone 12 Pro Max	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	15.4	1.45
iPhone 13	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	10.8	2.53
iPhone 13 Pro Max	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	10.4	2.55
iPhone 14	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	8.6	2.57
iPhone 14 Pro Max	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	7.9	2.69
iPad Pro (M1)	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	11.2	2.19
iPad Pro (M2)	`CPU_AND_NE`	`SPLIT_EINSUM_V2`	7.0	3.07

Details (Click to expand)

This benchmark was conducted by Apple using public beta versions of iOS 17.0, iPadOS 17.0 and macOS 14.0 Seed 8 in August 2023.
The performance data was collected by running the StableDiffusion Swift pipeline.
Swift code is not fully optimized, introducing up to ~10% overhead unrelated to Core ML model execution.
The median latency value across 5 back-to-back end-to-end executions are reported
The image generation procedure follows the standard configuration: 20 inference steps, 512x512 output image resolution, 77 text token sequence length, classifier-free guidance (batch size of 2 for unet).
The actual prompt length does not impact performance because the Core ML model is converted with a static shape that computes the forward pass for all of the 77 elements (tokenizer.model_max_length) in the text token sequence regardless of the actual length of the input text.
Weights are compressed to 6 bit precision. Please refer to this section for details.
Activations are in float16 precision for both the GPU and the Neural Engine.
For iPhone 12 mini and iPhone 13, we enable the reduceMemory option to load and unload models just-in-time to avoid memory issues. This added up to 2 seconds to the end-to-end latency.
In the benchmark table, we report the best performing --compute-unit and --attention-implementation values per device. The former does not modify the Core ML model and can be applied during runtime. The latter modifies the Core ML model. Note that the best performing compute unit is model version and hardware-specific.
Note that the performance optimizations in this repository (e.g. --attention-implementation) are generally applicable to Transformers and not customized to Stable Diffusion. Better performance may be observed upon custom kernel tuning. Therefore, these numbers do not represent peak HW capability.
Performance may vary across different versions of Stable Diffusion due to architecture changes in the model itself. Each reported number is specific to the model version mentioned in that context.
Performance may vary due to factors like increased system load from other applications or suboptimal device thermal state.

stabilityai/stable-diffusion-xl-base-1.0 Benchmark:

Device	`--compute-unit`	`--attention-implementation`	End-to-End Latency (s)	Diffusion Speed (iter/s)
MacBook Pro (M1 Max)	`CPU_AND_GPU`	`ORIGINAL`	46	0.46
MacBook Pro (M2 Max)	`CPU_AND_GPU`	`ORIGINAL`	37	0.57
Mac Studio (M1 Ultra)	`CPU_AND_GPU`	`ORIGINAL`	25	0.89
Mac Studio (M2 Ultra)	`CPU_AND_GPU`	`ORIGINAL`	20	1.11

Details (Click to expand)

This benchmark was conducted by Apple and Hugging Face using public beta versions of iOS 17.0, iPadOS 17.0 and macOS 14.0 in July 2023.
The performance data was collected by running the StableDiffusion Swift pipeline.
The median latency value across 3 back-to-back end-to-end executions are reported
The image generation procedure follows the standard configuration: 20 inference steps, 1024x1024 output image resolution, classifier-free guidance (batch size of 2 for unet).
Weights and activations are in float16 precision
Performance may vary across different versions of Stable Diffusion due to architecture changes in the model itself. Each reported number is specific to the model version mentioned in that context.
Performance may vary due to factors like increased system load from other applications or suboptimal device thermal state. Given these factors, we do not report sub-second variance in latency.

Weight Compression

Details (Click to expand)

coremltools-7.0 supports advanced weight compression techniques for pruning, palettization and linear 8-bit quantization. For these techniques, coremltools.optimize.torch.* includes APIs that require fine-tuning to maintain accuracy at higher compression rates whereas coremltools.optimize.coreml.* includes APIs that are applied post-training and are data-free.

We demonstrate how data-free post-training palettization implemented in coremltools.optimize.coreml.palettize_weights enables us to achieve greatly improved performance for Stable Diffusion on mobile devices. This API implements the Fast Exact k-Means algorithm for optimal weight clustering which yields more accurate palettes. Using --quantize-nbits {2,4,6,8} during conversion is going to apply this compression to the unet and text_encoder models.

For best results, we recommend training-time palettization: coremltools.optimize.torch.palettization.DKMPalettizer if fine-tuning your model is feasible. This API implements the Differentiable k-Means (DKM) learned palettization algorithm. In this exercise, we stick to post-training palettization for the sake of simplicity and ease of reproducibility.

The Neural Engine is capable of accelerating models with low-bit palettization: 1, 2, 4, 6 or 8 bits. With iOS 17 and macOS 14, compressed weights for Core ML models can be just-in-time decompressed during runtime (as opposed to ahead-of-time decompression upon load) to match the precision of activation tensors. This yields significant memory savings and enables models to run on devices with smaller RAM (e.g. iPhone 12 Mini). In addition, compressed weights are faster to fetch from memory which reduces the latency of memory bandwidth-bound layers. The just-in-time decompression behavior depends on the compute unit, layer type and hardware generation.

Weight Precision	`--compute-unit`	`stabilityai/stable-diffusion-2-1-base` generating "a high quality photo of a surfing dog"
6-bit	cpuAndNeuralEngine
16-bit	cpuAndNeuralEngine
16-bit	cpuAndGPU

Note that there are minor differences across 16-bit (float16) and 6-bit results. These differences are comparable to the differences across float16 and float32 or differences across compute units as exemplified above. We recommend a minimum of 6 bits for palettizing Stable Diffusion. Smaller number of bits (1, 2 and 4) will require either fine-tuning or advanced palettization techniques such as MBP.

Resources:

MBP: Post-Training Mixed-Bit Palettization

Details (Click to expand)

3.41-bit	4.50-bit	6.55-bit	16-bit (original)

This is an advanced compression technique that picks a suitable number of bits (among 1, 2, 4, 6 and 8) in order to achieve the desired signal strength as measured by end-to-end PSNR. The compression rate depends on the model version as well as the tolerance for signal loss (drop in PSNR).

For example, the original float16 stabilityai/stable-diffusion-xl-base-1.0 model has an ~82 dB signal strength. Naively applying linear 8-bit quantization to the Unet model drops the signal to ~65 dB. Instead, applying MBP yields an average of 2.81-bits quantization while maintaining a signal strength of ~67 dB. This technique generally yields better results compared to using --quantize-nbits during model conversion but requires a "pre-analysis" run that takes up to a few hours on a single GPU (mps or cuda).

Here is the signal strength (PSNR in dB) versus model size reduction (% of float16 size) for stabilityai/stable-diffusion-xl-base-1.0. The {1,2,4,6,8}-bit curves are generated by progresssively palettizing more layers using a palette with fixed number of bits. The layers were ordered in ascending order of their isolated impact to end-to-end signal strength so the cumulative compression's impact is delayed as much as possible. The mixed-bit curve is based on falling back to a higher number of bits as soon as a layer's isolated impact to end-to-end signal integrity drops below a threshold. Note that all curves based on palettization outperform linear 8-bit quantization at the same model size except for 1-bit.

Here are the steps for applying this technique on another model version:

Step 1: Run the pre-analysis script to generate "recipes" with varying signal strength:

python -m python_coreml_stable_diffusion.mixed_bit_compression_pre_analysis --model-version <model-version> -o <output-dir>

For popular base models, you may find the pre-computed pre-analysis results here. Fine-tuned models models are likely to honor the recipes of their corresponding base models but this is untested.

Step 2: The resulting JSON file from Step 1 will list "baselines", e.g.:

{
  "model_version": "stabilityai/stable-diffusion-xl-base-1.0",
  "baselines": {
    "original": 82.2,
    "linear_8bit": 66.025,
    "recipe_6.55_bit_mixedpalette": 79.9,
    "recipe_5.52_bit_mixedpalette": 78.2,
    "recipe_4.89_bit_mixedpalette": 76.8,
    "recipe_4.41_bit_mixedpalette": 75.5,
    "recipe_4.04_bit_mixedpalette": 73.2,
    "recipe_3.67_bit_mixedpalette": 72.2,
    "recipe_3.32_bit_mixedpalette": 71.4,
    "recipe_3.19_bit_mixedpalette": 70.4,
    "recipe_3.08_bit_mixedpalette": 69.6,
    "recipe_2.98_bit_mixedpalette": 68.6,
    "recipe_2.90_bit_mixedpalette": 67.8,
    "recipe_2.83_bit_mixedpalette": 67.0,
    "recipe_2.71_bit_mixedpalette": 66.3
  },
}

Among these baselines, select a recipe based on your desired signal strength. We recommend palettizing to ~4 bits depending on the use case even if the signal integrity for lower bit values are higher than the linear 8-bit quantization baseline.

Finally, apply the selected recipe to the float16 Core ML model as follows:

python -m python_coreml_stable_diffusion.mixed_bit_compression_apply --mlpackage-path <path-to-float16-unet-mlpackage> -o <output-dir> --pre-analysis-json-path <path-to--pre-analysis-json> --selected-recipe <selected-recipe-string-key>

An example <selected-recipe-string-key> would be "recipe_4.50_bit_mixedpalette" which achieves an average of 4.50-bits compression (compressed from ~5.2GB to ~1.46GB for SDXL). Please note that signal strength does not directly map to image-text alignment. Always verify that your MBP-compressed model variant is accurately generating images for your test prompts.

Using Stable Diffusion XL

Details (Click to expand)

Model Conversion

e.g.:

python -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 --attention-implementation ORIGINAL -o <output-dir>

--xl-version: Additional argument to pass to the conversion script when specifying an XL model
--attention-implementation ORIGINAL (recommended for cpuAndGPU)
Due to known float16 overflow issues in the VAE, it runs in float32 precision for now

Inference

e.g.

swift run StableDiffusionSample <prompt> --resource-path <output-mlpackages-directory/Resources> --output-path <output-dir> --compute-units cpuAndGPU --xl

Only --compute-units cpuAndGPU is supported for now
Only the base model is supported, refiner model is not yet supported
ControlNet for XL is not yet supported

Using ControlNet

Example results using the prompt "a high quality photo of a surfing dog" conditioned on the scribble (leftmost):

ControlNet allows users to condition image generation with Stable Diffusion on signals such as edge maps, depth maps, segmentation maps, scribbles and pose. Thanks to @ryu38's contribution, both the Python CLI and the Swift package support ControlNet models. Please refer to this section for details on setting up Stable Diffusion with ControlNet.

Using the System Multilingual Text Encoder

Details (Click to expand)

With iOS 17 and macOS 14, NaturalLanguage framework introduced the NLContextualEmbedding which provides Transformer-based textual embeddings for Latin (20 languages), Cyrillic (4 languages) and CJK (3 languages) scripts. The WWDC23 session titled Explore Natural Language multilingual models demonstrated how this powerful new model can be used by developers to train downstream tasks such as multilingual image generation with Stable Diffusion.

The code to reproduce this demo workflow is made available in this repository. There are several ways in which this workflow can be implemented. Here is an example:

Step 1: Curate an image-text dataset with the desired languages.

Step 2: Pre-compute the NLContextualEmbedding values and replace the text strings with these embedding vectors in your dataset.

Step 3: Fine-tune a base model from Hugging Face Hub that is compatible with the StableDiffusionPipeline by using your new dataset and replacing the default text_encoder with your pre-computed NLContextualEmbedding values.

Step 4: In order to be able to swap the text_encoder of a base model without training new layers, the base model's text_encoder.hidden_size must match that of NLContextualEmbedding. If it doesn't, you will need to train a linear projection layer to map between the two dimensionalities. After fine-tuning, this linear layer should be converted to CoreML as follows:

python -m python_coreml_stable_diffusion.multilingual_projection --input-path <path-to-projection-torchscript> --output-dir <output-dir>

The command above will yield a MultilingualTextEncoderProjection.mlmodelc file under --output-dir and this should be colocated with the rest of the Core ML model assets that were generated through --bundle-resources-for-swift-cli.

Step 5: The multilingual system text encoder can now be invoked by setting useMultilingualTextEncoder to true when initializing a pipeline or setting --use-multilingual-text-encoder in the CLI. Note that the model assets are distributed over-the-air so the first invocation will trigger asset downloads which is less than 100MB.

Resources:

Using Ready-made Core ML Models from Hugging Face Hub

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🤗 Hugging Face ran the conversion procedure on the following models and made the Core ML weights publicly available on the Hub. If you would like to convert a version of Stable Diffusion that is not already available on the Hub, please refer to the Converting Models to Core ML.

6-bit quantized models (suitable for iOS 17 and macOS 14):
Mixed-bit quantized models

stabilityai/stable-diffusion-xl-base-1.0

Uncompressed models:

If you want to use any of those models you may download the weights and proceed to generate images with Python or Swift.

There are several variants in each model repository. You may clone the whole repos using git and git lfs to download all variants, or selectively download the ones you need.

To clone the repos using git, please follow this process:

Step 1: Install the git lfs extension for your system.

git lfs stores large files outside the main git repo, and it downloads them from the appropriate server after you clone or checkout. It is available in most package managers, check the installation page for details.

Step 2: Enable git lfs by running this command once:

git lfs install

Step 3: Use git clone to download a copy of the repo that includes all model variants. For Stable Diffusion version 1.4, you'd issue the following command in your terminal:

git clone https://huggingface.co/apple/coreml-stable-diffusion-v1-4

If you prefer to download specific variants instead of cloning the repos, you can use the huggingface_hub Python library. For example, to do generation in Python using the ORIGINAL attention implementation (read this section for details), you could use the following helper code:

from huggingface_hub import snapshot_download
from pathlib import Path

repo_id = "apple/coreml-stable-diffusion-v1-4"
variant = "original/packages"

model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
print(f"Model downloaded at {model_path}")

model_path would be the path in your local filesystem where the checkpoint was saved. Please, refer to this post for additional details.

Converting Models to Core ML

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Step 1: Create a Python environment and install dependencies:

conda create -n coreml_stable_diffusion python=3.8 -y
conda activate coreml_stable_diffusion
cd /path/to/cloned/ml-stable-diffusion/repository
pip install -e .

Step 2: Log in to or register for your Hugging Face account, generate a User Access Token and use this token to set up Hugging Face API access by running huggingface-cli login in a Terminal window.

Step 3: Navigate to the version of Stable Diffusion that you would like to use on Hugging Face Hub and accept its Terms of Use. The default model version is CompVis/stable-diffusion-v1-4. The model version may be changed by the user as described in the next step.

Step 4: Execute the following command from the Terminal to generate Core ML model files (.mlpackage)

python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --convert-text-encoder --convert-vae-decoder --convert-safety-checker --model-version <model-version-string-from-hub> -o <output-mlpackages-directory>

WARNING: This command will download several GB worth of PyTorch checkpoints from Hugging Face. Please ensure that you are on Wi-Fi and have enough disk space.

This generally takes 15-20 minutes on an M1 MacBook Pro. Upon successful execution, the 4 neural network models that comprise Stable Diffusion will have been converted from PyTorch to Core ML (.mlpackage) and saved into the specified <output-mlpackages-directory>. Some additional notable arguments:

--model-version: The model version name as published on the Hugging Face Hub
--bundle-resources-for-swift-cli: Compiles all 4 models and bundles them along with necessary resources for text tokenization into <output-mlpackages-directory>/Resources which should provided as input to the Swift package. This flag is not necessary for the diffusers-based Python pipeline.
--quantize-nbits: Quantizes the weights of unet and text_encoder models down to 2, 4, 6 or 8 bits using a globally optimal k-means clustering algorithm. By default all models are weight-quantized to 16 bits even if this argument is not specified. Please refer to [this section](#compression for details and further guidance on weight compression.
--chunk-unet: Splits the Unet model in two approximately equal chunks (each with less than 1GB of weights) for mobile-friendly deployment. This is required for Neural Engine deployment on iOS and iPadOS if weights are not quantized to 6-bits or less (--quantize-nbits {2,4,6}). This is not required for macOS. Swift CLI is able to consume both the chunked and regular versions of the Unet model but prioritizes the former. Note that chunked unet is not compatible with the Python pipeline because Python pipeline is intended for macOS only.
--attention-implementation: Defaults to SPLIT_EINSUM which is the implementation described in Deploying Transformers on the Apple Neural Engine. --attention-implementation SPLIT_EINSUM_V2 yields 10-30% improvement for mobile devices, still targeting the Neural Engine. --attention-implementation ORIGINAL will switch to an alternative implementation that should be used for CPU or GPU deployment on some Mac devices. Please refer to the Performance Benchmark section for further guidance.
--check-output-correctness: Compares original PyTorch model's outputs to final Core ML model's outputs. This flag increases RAM consumption significantly so it is recommended only for debugging purposes.
--convert-controlnet: Converts ControlNet models specified after this option. This can also convert multiple models if you specify like --convert-controlnet lllyasviel/sd-controlnet-mlsd lllyasviel/sd-controlnet-depth.
--unet-support-controlnet: enables a converted UNet model to receive additional inputs from ControlNet. This is required for generating image with using ControlNet and saved with a different name, *_control-unet.mlpackage, distinct from normal UNet. On the other hand, this UNet model can not work without ControlNet. Please use normal UNet for just txt2img.
--convert-vae-encoder: not required for text-to-image applications. Required for image-to-image applications in order to map the input image to the latent space.

Image Generation with Python

Click to expand

Run text-to-image generation using the example Python pipeline based on diffusers:

python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i <output-mlpackages-directory> -o </path/to/output/image> --compute-unit ALL --seed 93

Please refer to the help menu for all available arguments: python -m python_coreml_stable_diffusion.pipeline -h. Some notable arguments:

-i: Should point to the -o directory from Step 4 of Converting Models to Core ML section from above.
--model-version: If you overrode the default model version while converting models to Core ML, you will need to specify the same model version here.
--compute-unit: Note that the most performant compute unit for this particular implementation may differ across different hardware. CPU_AND_GPU or CPU_AND_NE may be faster than ALL. Please refer to the Performance Benchmark section for further guidance.
--scheduler: If you would like to experiment with different schedulers, you may specify it here. For available options, please see the help menu. You may also specify a custom number of inference steps by --num-inference-steps which defaults to 50.
--controlnet: ControlNet models specified with this option are used in image generation. Use this option in the format --controlnet lllyasviel/sd-controlnet-mlsd lllyasviel/sd-controlnet-depth and make sure to use --controlnet-inputs in conjunction.
--controlnet-inputs: Image inputs corresponding to each ControlNet model. Please provide image paths in same order as models in --controlnet, for example: --controlnet-inputs image_mlsd image_depth.

Image Generation with Swift

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Example CLI Usage

swift run StableDiffusionSample "a photo of an astronaut riding a horse on mars" --resource-path <output-mlpackages-directory>/Resources/ --seed 93 --output-path </path/to/output/image>

The output will be named based on the prompt and random seed: e.g. </path/to/output/image>/a_photo_of_an_astronaut_riding_a_horse_on_mars.93.final.png

Please use the --help flag to learn about batched generation and more.

Example Library Usage

import StableDiffusion
...
let pipeline = try StableDiffusionPipeline(resourcesAt: resourceURL)
pipeline.loadResources()
let image = try pipeline.generateImages(prompt: prompt, seed: seed).first

On iOS, the reduceMemory option should be set to true when constructing StableDiffusionPipeline

Swift Package Details

This Swift package contains two products:

StableDiffusion library
StableDiffusionSample command-line tool

Both of these products require the Core ML models and tokenization resources to be supplied. When specifying resources via a directory path that directory must contain the following:

TextEncoder.mlmodelc (text embedding model)
Unet.mlmodelc or UnetChunk1.mlmodelc & UnetChunk2.mlmodelc (denoising autoencoder model)
VAEDecoder.mlmodelc (image decoder model)
vocab.json (tokenizer vocabulary file)
merges.text (merges for byte pair encoding file)

Optionally, for image2image, in-painting, or similar:

VAEEncoder.mlmodelc (image encoder model)

Optionally, it may also include the safety checker model that some versions of Stable Diffusion include:

SafetyChecker.mlmodelc

Optionally, for ControlNet:

ControlledUNet.mlmodelc or ControlledUnetChunk1.mlmodelc & ControlledUnetChunk2.mlmodelc (enabled to receive ControlNet values)
controlnet/ (directory containing ControlNet models)
- LllyasvielSdControlnetMlsd.mlmodelc (for example, from lllyasviel/sd-controlnet-mlsd)
- LllyasvielSdControlnetDepth.mlmodelc (for example, from lllyasviel/sd-controlnet-depth)
- Other models you converted

Note that the chunked version of Unet is checked for first. Only if it is not present will the full Unet.mlmodelc be loaded. Chunking is required for iOS and iPadOS and not necessary for macOS.

Example Swift App

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🤗 Hugging Face created an open-source demo app on top of this library. It's written in native Swift and Swift UI, and runs on macOS, iOS and iPadOS. You can use the code as a starting point for your app, or to see how to integrate this library in your own projects.

Hugging Face has made the app available in the Mac App Store.

FAQ

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Q1: ERROR: Failed building wheel for tokenizers or error: can't find Rust compiler

A1: Please review this potential solution.

Q2: RuntimeError: {NSLocalizedDescription = "Error computing NN outputs."

A2: There are many potential causes for this error. In this context, it is highly likely to be encountered when your system is under increased memory pressure from other applications. Reducing memory utilization of other applications is likely to help alleviate the issue.

Q3: My Mac has 8GB RAM and I am converting models to Core ML using the example command. The process is getting killed because of memory issues. How do I fix this issue?

A3: In order to minimize the memory impact of the model conversion process, please execute the following command instead:

python -m python_coreml_stable_diffusion.torch2coreml --convert-vae-encoder --model-version <model-version-string-from-hub> -o <output-mlpackages-directory> && \
python -m python_coreml_stable_diffusion.torch2coreml --convert-vae-decoder --model-version <model-version-string-from-hub> -o <output-mlpackages-directory> && \
python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --model-version <model-version-string-from-hub> -o <output-mlpackages-directory> && \
python -m python_coreml_stable_diffusion.torch2coreml --convert-text-encoder --model-version <model-version-string-from-hub> -o <output-mlpackages-directory> && \
python -m python_coreml_stable_diffusion.torch2coreml --convert-safety-checker --model-version <model-version-string-from-hub> -o <output-mlpackages-directory> &&

If you need --chunk-unet, you may do so in yet another independent command which will reuse the previously exported Unet model and simply chunk it in place:

python -m python_coreml_stable_diffusion.torch2coreml --convert-unet --chunk-unet -o <output-mlpackages-directory>

Q4: My Mac has 8GB RAM, should image generation work on my machine?

A4: Yes! Especially the --compute-unit CPU_AND_NE option should work under reasonable system load from other applications. Note that part of the Example Results were generated using an M2 MacBook Air with 8GB RAM.

Q5: Every time I generate an image using the Python pipeline, loading all the Core ML models takes 2-3 minutes. Is this expected?

A5: Yes and using the Swift library reduces this to just a few seconds. The reason is that coremltools loads Core ML models (.mlpackage) and each model is compiled to be run on the requested compute unit during load time. Because of the size and number of operations of the unet model, it takes around 2-3 minutes to compile it for Neural Engine execution. Other models should take at most a few seconds. Note that coremltools does not cache the compiled model for later loads so each load takes equally long. In order to benefit from compilation caching, StableDiffusion Swift package by default relies on compiled Core ML models (.mlmodelc) which will be compiled down for the requested compute unit upon first load but then the cache will be reused on subsequent loads until it is purged due to lack of use.

If you intend to use the Python pipeline in an application, we recommend initializing the pipeline once so that the load time is only incurred once. Afterwards, generating images using different prompts and random seeds will not incur the load time for the current session of your application.

Q6: I want to deploy StableDiffusion, the Swift package, in my mobile app. What should I be aware of?

A6: The Image Generation with Swift section describes the minimum SDK and OS versions as well as the device models supported by this package. We recommend carefully testing the package on the device with the least amount of RAM available among your deployment targets.

The image generation process in StableDiffusion can yield over 2 GB of peak memory during runtime depending on the compute units selected. On iPadOS, we recommend using .cpuAndNeuralEngine in your configuration and the reduceMemory option when constructing a StableDiffusionPipeline to minimize memory pressure.

If your app crashes during image generation, consider adding the Increased Memory Limit capability to inform the system that some of your app’s core features may perform better by exceeding the default app memory limit on supported devices.

On iOS, depending on the iPhone model, Stable Diffusion model versions, selected compute units, system load and design of your app, this may still not be sufficient to keep your apps peak memory under the limit. Please remember, because the device shares memory between apps and iOS processes, one app using too much memory can compromise the user experience across the whole device.

We strongly recommend quantizing your models with --quantize-nbits 6 for iOS deployment. This reduces the peak RAM usage by 1GB or more depending on the model version and robustly enables inference even on iPhone 12 Mini.

Q7: How do I generate images with different resolutions using the same Core ML models?

A7: The current version of python_coreml_stable_diffusion does not support single-model multi-resolution out of the box. However, developers may fork this project and leverage the flexible shapes support from coremltools to extend the torch2coreml script by using coremltools.EnumeratedShapes. Note that, while the text_encoder is agnostic to the image resolution, the inputs and outputs of vae_decoder and unet models are dependent on the desired image resolution.

Q8: Are the Core ML and PyTorch generated images going to be identical?

A8: If desired, the generated images across PyTorch and Core ML can be made approximately identical. However, it is not guaranteed by default. There are several factors that might lead to different images across PyTorch and Core ML:

1. Random Number Generator Behavior

The main source of potentially different results across PyTorch and Core ML is the Random Number Generator (RNG) behavior. PyTorch and Numpy have different sources of randomness. python_coreml_stable_diffusion generally relies on Numpy for RNG (e.g. latents initialization) and StableDiffusion Swift Library reproduces this RNG behavior by default. However, PyTorch-based pipelines such as Hugging Face diffusers relies on PyTorch's RNG behavior. Thanks to @liuliu's contribution, one can match the PyTorch (CPU) RNG behavior in Swift by specifying --rng torch which selects the torchRNG mode.

2. PyTorch

"Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds." (source).

3. Model Function Drift During Conversion

The difference in outputs across corresponding PyTorch and Core ML models is a potential cause. The signal integrity is tested during the conversion process (enabled via --check-output-correctness argument to python_coreml_stable_diffusion.torch2coreml) and it is verified to be above a minimum PSNR value as tested on random inputs. Note that this is simply a sanity check and does not guarantee this minimum PSNR across all possible inputs. Furthermore, the results are not guaranteed to be identical when executing the same Core ML models across different compute units. This is not expected to be a major source of difference as the sample visual results indicate in this section.

4. Weights and Activations Data Type

When quantizing models from float32 to lower-precision data types such as float16, the generated images are known to vary slightly in semantics even when using the same PyTorch model. Core ML models generated by coremltools have float16 weights and activations by default unless explicitly overridden. This is not expected to be a major source of difference.

Q9: The model files are very large, how do I avoid a large binary for my App?

A9: The recommended option is to prompt the user to download these assets upon first launch of the app. This keeps the app binary size independent of the Core ML models being deployed. Disclosing the size of the download to the user is extremely important as there could be data charges or storage impact that the user might not be comfortable with.

Q10: `Could not initialize NNPACK! Reason: Unsupported hardware`

A10: This warning is safe to ignore in the context of this repository.

Q11: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect

A11: This warning is safe to ignore in the context of this repository.

Q12: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown

A12: If this warning is printed right after zsh: killed python -m python_coreml_stable_diffusion.torch2coreml ... , then it is highly likely that your Mac has run out of memory while converting models to Core ML. Please see Q3 from above for the solution.

BibTeX Reference

@misc{stable-diffusion-coreml-apple-silicon,
title = {Stable Diffusion with Core ML on Apple Silicon},
author = {Atila Orhon and Michael Siracusa and Aseem Wadhwa},
year = {2022},
URL = {null}
}

Name		Name	Last commit message	Last commit date
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.github		.github
assets		assets
python_coreml_stable_diffusion		python_coreml_stable_diffusion
swift		swift
tests		tests
.gitignore		.gitignore
ACKNOWLEDGEMENTS		ACKNOWLEDGEMENTS
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE.md		LICENSE.md
Package.swift		Package.swift
README.md		README.md
requirements.txt		requirements.txt
setup.py		setup.py

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Core ML Stable Diffusion

System Requirements

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Weight Compression

MBP: Post-Training Mixed-Bit Palettization

Using Stable Diffusion XL

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Using the System Multilingual Text Encoder

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