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https://github.com/argmaxinc/DiffusionKit
On-device Inference of Diffusion Models for Apple Silicon
https://github.com/argmaxinc/DiffusionKit
Last synced: 1 day ago
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On-device Inference of Diffusion Models for Apple Silicon
- Host: GitHub
- URL: https://github.com/argmaxinc/DiffusionKit
- Owner: argmaxinc
- License: mit
- Created: 2024-06-04T21:12:40.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-10-29T17:37:19.000Z (14 days ago)
- Last Synced: 2024-10-29T18:55:46.951Z (14 days ago)
- Language: Python
- Homepage:
- Size: 1.15 MB
- Stars: 486
- Watchers: 13
- Forks: 29
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# DiffusionKit
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](https://github.com/ml-explore/mlx) in Python
- `DiffusionKit`, a Swift package for on-device inference of diffusion models using Core ML and MLX
## Installation
The following installation steps are required for:
- MLX inference
- PyTorch to Core ML model conversion
### Python Environment Setup
```bash
conda create -n diffusionkit python=3.11 -y
conda activate diffusionkit
cd /path/to/diffusionkit/repo
pip install -e .
```
### Hugging Face Hub Credentials
[Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium) requires users to accept the terms before downloading the checkpoint. Once you accept the terms, sign in with your Hugging Face hub READ token as below:
> [!IMPORTANT]
> If using a fine-grained token, it is also necessary to [edit permissions](https://huggingface.co/settings/tokens) to allow `Read access to contents of all public gated repos you can access`
```bash
huggingface-cli login --token YOUR_HF_HUB_TOKEN
```
## Converting Models from PyTorch to Core ML
Click to expand
**Step 1:** Follow the installation steps from the previous section
**Step 2:** Verify you've accepted the [StabilityAI license terms](https://huggingface.co/stabilityai/stable-diffusion-3-medium) and have allowed gated access on your [HuggingFace token](https://huggingface.co/settings/tokens)
**Step 3:** Prepare the denoise model (MMDiT) Core ML model files (`.mlpackage`)
```shell
python -m python.src.diffusionkit.tests.torch2coreml.test_mmdit --sd3-ckpt-path stabilityai/stable-diffusion-3-medium --model-version 2b -o --latent-size {64, 128}
```
**Step 4:** Prepare the VAE Decoder Core ML model files (`.mlpackage`)
```shell
python -m python.src.diffusionkit.tests.torch2coreml.test_vae --sd3-ckpt-path stabilityai/stable-diffusion-3-medium -o --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 local `sd3_medium.safetensors` file
## Image Generation with Python MLX
Click to expand
### CLI ###
For simple text-to-image in float16 precision:
```shell
diffusionkit-cli --prompt "a photo of a cat" --output-path --seed 0 --w16 --a16
```
Some notable optional arguments:
- For image-to-image, use `--image-path` (path to input image) and `--denoise` (value between 0. and 1.)
- T5 text embeddings, use `--t5`
- For different resolutions, use `--height` and `--width`
- For using a local checkpoint, use `--local-ckpt ` (e.g. `~/models/stable-diffusion-3-medium/sd3_medium.safetensors`).
Please refer to the help menu for all available arguments: `diffusionkit-cli -h`.
### Code ###
After installing the package, import it using:
```python
from diffusionkit.mlx import DiffusionPipeline
```
Then, initialize the pipeline object:
```python
pipeline = DiffusionPipeline(
model="argmaxinc/stable-diffusion",
w16=True,
shift=3.0,
use_t5=False,
model_size="2b",
low_memory_mode=False,
a16=True,
)
```
Some notable optional arguments:
- For T5 text embeddings, set `use_t5=True`
- For using a local checkpoint, set `local_ckpt=` (e.g. `~/models/stable-diffusion-3-medium/sd3_medium.safetensors`).
- If you want to use the `pipeline` object more than once, set `low_memory_mode=False`.
- For loading weights in FP32, set `w16=False`
- For FP32 activations, set `a16=False`
Note: Only `2b` model size is available for this pipeline.
Finally, to generate the image, use the `generate_image()` function:
```python
HEIGHT = 512
WIDTH = 512
image, _ = pipeline.generate_image(
"a photo of a cat holding a sign that says 'Hello!'",
cfg_weight=5.0,
num_steps=50,
latent_size=(HEIGHT // 8, WIDTH // 8),
)
```
Some notable optional arguments:
- For image-to-image, use `image_path` (path to input image) and `denoise` (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:
```python
image.save("path/to/save.png")
```
## Image Generation with Swift
Click to expand
### Core ML Swift
[Apple Core ML Stable Diffusion](https://github.com/apple/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.
### MLX Swift
🚧
## License
DiffusionKit is released under the MIT License. See [LICENSE](LICENSE) for more details.
## Citation
If you use DiffusionKit for something cool or just find it useful, please drop us a note at [[email protected]](mailto:[email protected])!
If you use DiffusionKit for academic work, here is the BibTeX:
```bibtex
@misc{diffusionkit-argmax,
title = {DiffusionKit},
author = {Argmax, Inc.},
year = {2024},
URL = {https://github.com/argmaxinc/DiffusionKit}
}
```