Run Diffusion Models on Apple Silicon with Core ML and MLX
This repository comprises
diffusionkit
, a Python package for converting PyTorch models to Core ML format and performing image generation with MLX in PythonDiffusionKit
, a Swift package for on-device inference of diffusion models using Core ML and MLX
The following installation steps are required for:
- MLX inference
- PyTorch to Core ML model conversion
conda create -n diffusionkit python=3.11 -y
conda activate diffusionkit
cd /path/to/diffusionkit/repo
pip install -e .
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Stable Diffusion 3 requires users to accept the terms before downloading the checkpoint.
FLUX.1-dev also requires users to accept the terms before downloading the checkpoint.
Once you accept the terms, # with your Hugging Face hub READ token as below:
[!IMPORTANT] If using a fine-grained token, it is also necessary to edit permissions to allow
Read access to contents of all public gated repos you can access
huggingface-cli login --token YOUR_HF_HUB_TOKEN
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Step 1: Follow the installation steps from the previous section
Step 2: Verify you've accepted the StabilityAI license terms and have allowed gated access on your HuggingFace token
Step 3: Prepare the denoise model (MMDiT) Core ML model files (.mlpackage
)
python -m python.src.diffusionkit.tests.torch2coreml.test_mmdit --sd3-ckpt-path stabilityai/stable-diffusion-3-medium --model-version 2b -o <output-mlpackages-directory> --latent-size {64, 128}
Step 4: Prepare the VAE Decoder Core ML model files (.mlpackage
)
python -m python.src.diffusionkit.tests.torch2coreml.test_vae --sd3-ckpt-path stabilityai/stable-diffusion-3-medium -o <output-mlpackages-directory> --latent-size {64, 128}
Note:
--sd3-ckpt-path
can be a path any HuggingFace repo (e.g.stabilityai/stable-diffusion-3-medium
) OR a path to a localsd3_medium.safetensors
file
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Most simple:
diffusionkit-cli --prompt "a photo of a cat" --output-path </path/to/output/image.png>
Some notable optional arguments for:
- Reproduciblity of results, use
--seed
- image-to-image, use
--image-path
(path to input image) and--denoise
(value between 0. and 1.) - Enabling T5 encoder in SD3, use
--t5
(FLUX must use T5 regardless of this argument) - Different resolutions, use
--height
and--width
- Using a local checkpoint, use
--local-ckpt </path/to/ckpt.safetensors>
(e.g.~/models/stable-diffusion-3-medium/sd3_medium.safetensors
).
Please refer to the help menu for all available arguments: diffusionkit-cli -h
.
Note: When using FLUX.1-dev
, verify you've accepted the FLUX.1-dev licence and have allowed gated access on your HuggingFace token
For Stable Diffusion 3:
from diffusionkit.mlx import DiffusionPipeline
pipeline = DiffusionPipeline(
shift=3.0,
use_t5=False,
model_version="argmaxinc/mlx-stable-diffusion-3-medium",
low_memory_mode=True,
a16=True,
w16=True,
)
For FLUX:
from diffusionkit.mlx import FluxPipeline
pipeline = FluxPipeline(
shift=1.0,
model_version="argmaxinc/mlx-FLUX.1-schnell", # model_version="argmaxinc/mlx-FLUX.1-dev" for FLUX.1-dev
low_memory_mode=True,
a16=True,
w16=True,
)
Finally, to generate the image, use the generate_image()
function:
HEIGHT = 512
WIDTH = 512
NUM_STEPS = 4 # 4 for FLUX.1-schnell, 50 for SD3 and FLUX.1-dev
CFG_WEIGHT = 0. # for FLUX.1-schnell, 5. for SD3
image, _ = pipeline.generate_image(
"a photo of a cat",
cfg_weight=CFG_WEIGHT,
num_steps=NUM_STEPS,
latent_size=(HEIGHT // 8, WIDTH // 8),
)
Some notable optional arguments:
- For image-to-image, use
image_path
(path to input image) anddenoise
(value between 0. and 1.) input variables. - For seed, use
seed
input variable. - For negative prompt, use
negative_text
input variable.
The generated image
can be saved with:
image.save("path/to/save.png")
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Apple Core ML Stable Diffusion is the initial Core ML backend for DiffusionKit. Stable Diffusion 3 support is upstreamed to that repository while we build the holistic Swift inference package.
🚧
DiffusionKit is released under the MIT License. See LICENSE for more details.
If you use DiffusionKit for something cool or just find it useful, please drop us a note at info@takeargmax.com!
If you use DiffusionKit for academic work, here is the BibTeX:
@misc{diffusionkit-argmax,
title = {DiffusionKit},
author = {Argmax, Inc.},
year = {2024},
URL = {https://github.com/argmaxinc/DiffusionKit}
}