Deploy Stable Diffusion XL 1.0 on AWS EC2

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This example demonstrates how to deploy a Stable Diffusion XL model from Hugging Face on AWS EC2 using Runhouse.

Setup credentials and dependencies

Optionally, set up a virtual environment:

$ conda create -n rh-sdxl python=3.9.15
$ conda activate rh-sdxl

Install the required dependencies:

$ pip install "runhouse[aws]" Pillow

We'll be launching an AWS EC2 instance via SkyPilot, so we need to make sure our AWS credentials are set up:

$ aws configure
$ sky check

We'll be downloading the Stable Diffusion model from Hugging Face, so we need to set up our Hugging Face token:

$ export HF_TOKEN=<your huggingface token>

Setting up a model class

We import runhouse and other required libraries:

import base64
import os
from io import BytesIO

import runhouse as rh
from PIL import Image

Next, we define a class that will hold the model and allow us to send prompts to it. We'll later wrap this with rh.module. This is a Runhouse class that allows you to run code in your class on a remote machine.

Learn more in the Runhouse docs on functions and modules.

class StableDiffusionXLPipeline:
    def __init__(
        self,
        model_id: str = "stabilityai/stable-diffusion-xl-base-1.0",
        model_dir: str = "sdxl",
    ):
        super().__init__()
        self.model_dir = model_dir
        self.model_id = model_id
        self.pipeline = None

    def _model_loaded_on_disk(self):
        return (
            self.model_dir
            and os.path.isdir(self.model_dir)
            and len(os.listdir(self.model_dir)) > 0
        )

    def _load_pipeline(self):
        import torch
        from diffusers import DiffusionPipeline
        from huggingface_hub import snapshot_download

        if not self._model_loaded_on_disk():
            # save compiled model to local directory
            # Downloads our compiled model from the HuggingFace Hub
            # and makes sure we exclude the symlink files and "hidden" files, like .DS_Store, .gitignore, etc.
            snapshot_download(
                self.model_id,
                local_dir=self.model_dir,
                local_dir_use_symlinks=False,
                allow_patterns=["[!.]*.*"],
            )

        # load local converted model into pipeline
        self.pipeline = DiffusionPipeline.from_pretrained(
            self.model_dir, device_ids=[0, 1], torch_dtype=torch.float16
        )
        self.pipeline.to("cuda")

    def generate(self, input_prompt: str, output_format: str = "JPEG", **parameters):
        # extract prompt from data
        if not self.pipeline:
            self._load_pipeline()

        generated_images = self.pipeline(input_prompt, **parameters)["images"]

        # postprocess convert image into base64 string
        encoded_images = []
        for image in generated_images:
            buffered = BytesIO()
            image.save(buffered, format=output_format)
            encoded_images.append(base64.b64encode(buffered.getvalue()).decode())

        # always return the first
        return encoded_images


def decode_base64_image(image_string):
    base64_image = base64.b64decode(image_string)
    buffer = BytesIO(base64_image)
    return Image.open(buffer)

Setting up Runhouse primitives

Now, we define the main function that will run locally when we run this script, and set up our Runhouse module on a remote cluster. First, we create a cluster with the desired instance type and provider. Our instance_type here is defined as g5.8xlarge, which is an AWS instance type. We can alternatively specify an accelerator type and count, such as A10G:1, and any instance type with those specifications will be used.

Learn more in the Runhouse docs on clusters.

Note

Make sure that your code runs within a if __name__ == "__main__": block, as shown below. Otherwise, the script code will run when Runhouse attempts to run code remotely.

if __name__ == "__main__":

    cluster = rh.cluster(
        name="rh-g5",
        instance_type="g5.8xlarge",
        provider="aws",
    ).up_if_not()

Next, we define the environment for our module. This includes the required dependencies that need to be installed on the remote machine, as well as any secrets that need to be synced up from local to remote. Passing huggingface to the secrets parameter will load the Hugging Face token we set up earlier.

Learn more in the Runhouse docs on envs.

env = rh.env(
    name="sdxl_inference",
    reqs=[
        "diffusers==0.21.4",
        "huggingface_hub",
        "torch",
        "transformers==4.31.0",
        "accelerate==0.21.0",
    ],
    secrets=["huggingface"],  # Needed to download model
    env_vars={"NEURON_RT_NUM_CORES": "2"},
)

Finally, we define our module and run it on the remote cluster. We construct it normally and then call to to run it on the remote cluster. Alternatively, we could first check for an existing instance on the cluster by calling cluster.get(name="sdxl"). This would return the remote model after an initial run. If we want to update the module each time we run this script, we prefer to use to.

Note that we also pass the env object to the to method, which will ensure that the environment is set up on the remote machine before the module is run.

RemoteStableDiffusion = rh.module(StableDiffusionXLPipeline).to(
    cluster, env=env, name="StableDiffusionXLPipeline"
)
remote_sdxl = RemoteStableDiffusion(name="sdxl")

Calling our remote function

We can call the generate method on the model class instance if it were running locally. This will run the function on the remote cluster and return the response to our local machine automatically. Further calls will also run on the remote machine, and maintain state that was updated between calls, like self.model.

prompt = "A woman runs through a large, grassy field towards a house."
response = remote_sdxl.generate(
    prompt,
    num_inference_steps=25,
    negative_prompt="disfigured, ugly, deformed",
)

for gen_img in response:
    img = decode_base64_image(gen_img)
    img.show()