amedhussaini / stable-diffusion-bentoml

Deploy Your Own Stable Diffusion Service

Serving Stable Diffusion with BentoML

Stable Diffusion is an open-source text-to-image model released by stability.ai. It enables you to generate creative arts from natural language prompts in just seconds. Follow the steps in this repository to create a production-ready Stable Diffusion service with BentoML and deploy it to AWS EC2.

Prepare the Environment

If you don't wish to build the bento from scratch, feel free to download one of the pre-built bentos.

Clone repository and install dependencies:

git clone https://github.com/bentoml/stable-diffusion-bentoml.git && cd stable-diffusion-bentoml
python3 -m venv venv && . venv/bin/activate
pip install -U pip
pip install -r requirements.txt

🎉 Environment is ready!

Create the Stable Diffusion Bento

Here you can choose to either download pre-built Stable Diffusion bentos or build bentos from the Stable Diffusion models.

Download Pre-built Stable Diffusion Bentos

• Download fp32 bento (for CPU or GPU with more than 10GB VRAM)

  curl -O https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_fp32.bento && bentoml import ./sd_fp32.bento

• Download fp16 bento (for GPU with less than 10GB VRAM)

  curl -O https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_fp16.bento && bentoml import ./sd_fp16.bento

🎉 The Stable Diffusion bento is imported. You can advance to the "Deploy the Stable Diffusion Bento to EC2" section.

Build from Stable Diffusion Models

Choose a Stable Diffusion model

• fp32 (for CPU or GPU with more than 10GB VRAM)

   cd fp32/

• fp16 (for GPU with less than 10GB VRAM)

   cd fp16/

Download the Stable Diffusion model

• For fp32 model:

   # if tar and gzip is availabe
   curl https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_model_v1_4.tgz | tar zxf - -C models/
  
   # or if unzip is availabe
   curl -O https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_model_v1_4.zip && unzip -d models/ sd_model_v1_4.zip

• For fp16 model:

   # if tar and gzip is availabe
   curl https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_model_v1_4_fp16.tgz | tar zxf - -C models/
  
   # or if unzip is availabe
   curl -O https://s3.us-west-2.amazonaws.com/bentoml.com/stable_diffusion_bentoml/sd_model_v1_4_fp16.zip && unzip -d models/ sd_model_v1_4_fp16.zip

Run and test the BentoML service:

• Bring up the BentoML service with the following command.

   BENTOML_CONFIG=configuration.yaml bentoml serve service:svc --production

• Then you can run one of the scripts to test the service.

   ../txt2img_test.sh
   ../img2img_test.sh

Build a bento:

bentoml build

Building BentoML service "stable_diffusion_fp32:abclxar26s44kcvj" from build context "/Users/ssheng/github/stable-diffusion-bentoml/fp32"
Locking PyPI package versions..

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Successfully built Bento(tag="stable_diffusion_fp32:abclxar26s44kcvj")

🎉 The Stable Diffusion bento has been built! You can advance to the "Deploy the Stable Diffusion Bento to EC2" section.

Deploy the Stable Diffusion Bento to EC2

We will be using bentoctl to deploy the bento to EC2. bentoctl helps deploy your bentos into any cloud platform easily. Install the AWS EC2 operator to generate and apply Terraform files to EC2.

bentoctl operator install aws-ec2

The deployment has already been configured for you in the deployment_config.yaml file. By default bentoctl is configured to deploy the model on a g4dn.xlarge instance with Deep Learning AMI GPU PyTorch 1.12.0 (Ubuntu 20.04) AMI on us-west-1.

Note: This default configuration only works in the us-west-1 region. Choose the corresponding AMI Id in your region from AWS AMI Catalog to deploy to your desired region.

Generate the Terraform files.

# In the /bentoctl directory
bentoctl generate -f deployment_config.yaml

✨ generated template files.
  - ./main.tf
  - ./bentoctl.tfvars

Build the Docker image and push to AWS ECR.

bentoctl build -b stable_diffusion_fp32:latest -f deployment_config.yaml

🚀 Image pushed!
✨ generated template files.
  - ./bentoctl.tfvars
  - ./startup_script.sh
  
There is also an experimental command that you can use.
To create the resources specifed run this after the build command.
$ bentoctl apply

To cleanup all the resources created and delete the registry run
$ bentoctl destroy

Apply the Terraform files to deploy to AWS EC2. Head over to the endpoint URL displayed at the end and you can see your Stable Diffusion service is up and running. Run some test prompts to make sure everything is working.

bentoctl apply -f deployment_config.yaml

Apply complete! Resources: 2 added, 0 changed, 0 destroyed.

Outputs:

ec2_instance_status = "running"
endpoint = "http://53.183.151.211"

Finally, delete the deployment if the Stable Diffusion BentoML service is no longer needed.

bentoctl destroy -f deployment_config.yaml