My Project

This project walks you through how you can deploy a ControleNet model with Stable Diffusion on SageMaker endpoint using Multi-Model approach and Single-model approach.

• For the multi-model deployment approach please run the ControlNet-MME.ipynb
• For the single mode deployment approach please run the ControlNet.ipynb

Set Up StreamLit Front-End

The streamlit app for this prioject is located in app_complete.py.

To run this Streamlit App on Sagemaker Studio follow the steps in the link below:

• Set Up SageMaker Studio
• Launch SageMaker Studio
• Clone this git repo into studio
• In the app_complete.py script, change the values for the variables ENDPOINT to the name of the endpoint hosting the models and BUCKET to the name of the bucket to hold the input and output images.
• Open a system terminal by clicking on Amazon SageMaker Studio and then System Terminal as shown in the diagram below
• 
• Navigate into the cloned repository directory using the cd command and run the command pip install -r requirements.txt to install the needed python libraries
• Run command python3 -m streamlit run app_complete.py to start the Streamlit server. Do not use the links generated by the command as they won't work in studio.
• To enter the Streamlit app, open and run the Streamlit_app.ipynb notebook. This will generate the appropiate link to enter your Streamlit app from SageMaker studio. Click on the link to enter your Streamlit app. Happy querying :)
• ⚠ Note: If you rerun the Streamlit server it may use a different port. Take not of the port used (port number is the last 4 digit number after the last : (colon)) and modify the port variable in the Streamlit_app.ipynb notebook to get the correct link.

To run this Streamlit App on AWS EC2 (I tested this on the Ubuntu Image)

• Create a new ec2 instance
• Expose TCP port range 8500-9000 on Inbound connections of the attached Security group to the ec2 instance. TCP port 8501 is needed for Streamlit to work. See image below
• 
• Connect to your ec2 instance
• Run the appropiate commands to update the ec2 instance (sudo apt update and sudo apt upgrade -for Ubuntu)
• Clone this git repo git clone [github_link]
• In the app_complete.py script, change the values for the variables ENDPOINT to the name of the endpoint hosting the models and BUCKET to the name of the bucket to hold the input and output images. Make use of the nano command to make this changes. e.g. nano app_complete.py
• Install python3 and pip if not already installed
• Install the dependencies in the requirements.txt file by running the command sudo pip install -r requirements.txt
• Run command python3 -m streamlit run app_complete.py
• Copy the external link and paste in a new browser tab

Note

When invoking a model for the first time in MME, it will take some time to load that model from s3 into the endpoint memory before inference happens, subsequent inference on same model will be faster. Also take into consideration compute instance memory size when deploying a MME. Models are cached in memory for faster inference, however, if memory is full, model(s) would be uncached to make space for the new target model for inference. For more info read Sagemaker MME. As of 7/31/2023. I hosted this model successfully on an ml.g5.4xlarge instance.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.