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stable_diffusion

README.md

Stable Diffusion

Stable Diffusion in MLX. The implementation was ported from Hugging Face's diffusers and model weights are downloaded directly from the Hugging Face hub. The implementation currently supports the following models:

out
Image generated using Stable Diffusion in MLX and the prompt 'A big red sign saying MLX in capital letters.'

Installation

The dependencies are minimal, namely:

  • huggingface-hub to download the checkpoints.
  • regex for the tokenization
  • tqdm, PIL, and numpy for the txt2image.py script

You can install all of the above with the requirements.txt as follows:

pip install -r requirements.txt

Usage

Although each component in this repository can be used by itself, the fastest way to get started is by using the StableDiffusion class from the stable_diffusion module.

import mlx.core as mx
from stable_diffusion import StableDiffusion

# This will download all the weights from HF hub and load the models in
# memory
sd = StableDiffusion()

# This creates a python generator that returns the latent produced by the
# reverse diffusion process.
#
# Because MLX is lazily evaluated iterating over this generator doesn't
# actually perform the computation until mx.eval() is called.
latent_generator = sd.generate_latents(
    "A photo of an astronaut riding a horse on Mars."
)

# Here we are evaluating each diffusion step but we could also evaluate
# once at the end.
for x_t in latent_generator:
    mx.eval(x_t)

# Now x_t is the last latent from the reverse process aka x_0. We can
# decode it into an image using the stable diffusion VAE.
im = sd.decode(x_t)

The above is essentially the implementation of the txt2image.py script in the root of the repository. You can use the script as follows:

python txt2image.py "A photo of an astronaut riding a horse on Mars." --n_images 4 --n_rows 2

You can select the model using --model argument. Currently supported models are sdxl (default) and sd.

Image 2 Image

There is also the option of generating images based on another image using the example script image2image.py. To do that an image is first encoded using the autoencoder to get its latent representation and then noise is added according to the forward diffusion process and the strength parameter. A strength of 0.0 means no noise and a strength of 1.0 means starting from completely random noise.

image2image

Generations with varying strength using the original image and the prompt 'A lit fireplace'.

The command to generate the above images is:

python image2image.py --strength 0.5 original.png 'A lit fireplace'

Note

image2image.py will automatically downsample your input image to guarantee that its dimensions are divisible by 64. If you want full control of this process, resize your image prior to using the script.

Memory constrained devices

The txt2image.py script by default loads the model in float16 which reduces significantly the required memory for image generation. However, since the Stable Diffusion XL UNet alone has 2.6B parameters in order to use it in devices with 8GB of RAM, quantization is practically necessary.

The txt2image.py script supports quantization using the -q or --quantize command line arguments. When quantization is used, the script quantizes the text encoder models to 4 bits and the unet to 8 bits. This allows generating images on an 8GB Mac Mini with no-swapping.

python txt2image.py --n_images 4 -q -v --output still-life.png "A painting of a vase on a wooden table, dark background, still life."

painting
Image generated using Stable Diffusion XL turbo in MLX with the above command on an 8GB M1 Mac mini