Deploy Stable Diffusion XL 1.0 on AWS Inferentia

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This example demonstrates how to deploy a Stable Diffusion XL model from Hugging Face on AWS Inferentia2 using Runhouse. AWS Inferentia2 instances are powered by AWS Neuron, a custom hardware accelerator for machine learning inference workloads. This example uses a model that was pre-compiled for AWS Neuron, and is available on the Hugging Face Hub.

Setup credentials and dependencies

Optionally, set up a virtual environment:

$ conda create -n rh-inf2 python=3.9.15 $ conda activate rh-inf2

Install the few required dependencies:

$ pip install -r requirements.txt

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 Llama2 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. You'll notice this class inherits from 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(rh.Module): def __init__( self, model_id: str = "aws-neuron/stable-diffusion-xl-base-1-0-1024x1024", model_dir: str = "sdxl_neuron", ): 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): from huggingface_hub import snapshot_download from optimum.neuron import NeuronStableDiffusionXLPipeline if not self._model_loaded_on_disk(): # save compiled model to local directory # Downloads our compiled model from the HuggingFace Hub # using the revision as neuron version reference # and makes sure we exclude the symlink files and "hidden" files, like .DS_Store, .gitignore, etc. snapshot_download( self.model_id, revision="2.15.0", local_dir=self.model_dir, local_dir_use_symlinks=False, allow_patterns=["[!.]*.*"], ) # load local converted model into pipeline self.pipeline = NeuronStableDiffusionXLPipeline.from_pretrained( self.model_dir, device_ids=[0, 1] ) def generate(self, input_prompt: str, output_format: str = "JPEG", **parameters): if not self.pipeline: self._load_pipeline() generated_images = self.pipeline(input_prompt, **parameters)["images"] if output_format == "PIL": return generated_images # postprocess convert image into base64 string encoded_images = [] for image in generated_images: buffered = BytesIO(), format=output_format) encoded_images.append(base64.b64encode(buffered.getvalue()).decode()) return encoded_images def decode_base64_image(image_string): base64_image = base64.b64decode(image_string) buffer = BytesIO(base64_image) return

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 inf2.8xlarge, which is one of the special AWS Inferentia2 instance types. We can alternatively specify an accelerator type and count, such as A10G:1, and any instance type with those specifications will be used.

We use a specific image_id, which in this case is the Deep Learning AMI Base Neuron which comes with the AWS Neuron drivers preinstalled. The image_id is region-specific. To change the region, use the AWS CLI command on the page above under "Query AMI-ID with AWSCLI."

The cluster we set up here also uses tls for the server_connection_type, which means that all communication will be over HTTPS and encrypted. We need to tell SkyPilot to open port 443 for this to work.

We also set den_auth to True, which means that we will use Runhouse Den to authenticate public requests to this cluster. This means that we can open this cluster to the public internet, and only people who have ran runhouse login and set up Runhouse accounts will be able to access it.

Learn more in the Runhouse docs on clusters.


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-inf2", instance_type="inf2.8xlarge", provider="aws", image_id="ami-0e0f965ee5cfbf89b", region="us-east-1", server_connection_type="tls", open_ports=[443], den_auth=True, ).up_if_not()

Set up dependencies

We can run commands directly on the cluster via Here, we set up the environment for our upcoming environment that installed some AWS-neuron specific libraries. The torch_neuronx library needs to be installed before the rest of the env is set up in order to avoid a [common error]( training-troubleshooting.html#protobuf-error-typeerror-descriptors-cannot-not-be-created-directly), so we run this first. [ "python -m pip config set global.extra-index-url", "python -m pip install neuronx-cc==2.* torch-neuronx==", ], )

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. We also can set environment variables, such as NEURON_RT_NUM_CORES which is required for AWS Neuron.

Learn more in the Runhouse docs on envs.

env = rh.env( name="sdxl_inference", reqs=[ "optimum-neuron==0.0.20", "diffusers==0.27.2", ], secrets=["huggingface"], # Needed to download Llama2 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 get_or_to to run it on the remote cluster. Using get_or_to allows us to load the exiting Module by the name sdxl_neuron if it was already put on the cluster. If we want to update the module each time we run this script, we can use to instead of get_or_to.

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

model = StableDiffusionXLPipeline().get_or_to(cluster, env=env, name="sdxl_neuron")

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 = model.generate( prompt, num_inference_steps=25, negative_prompt="disfigured, ugly, deformed", ) img = decode_base64_image(response[0])