Asynchronous AI Image Generator API Service

• License: (MIT), Copyright (C) 2024, Author Phil Chen
  • This is a example application the author of this repository is not liable for damages or losses arising from your use or inability to use the code.

Description

This repo provides an example of an AI Image Generator API that creates asynchronously 4 unique images based on your prompt and stores them in S3. The API leverages Amazon Titan Image Generator which is accessed via Amazon Bedrock. Generating the images asyncronously allows for a quicker experience when processing multiple images.

The API Overview:

1. Create a POST request to the generate_images endpoint, that has prompt, output_folder and file_name_prefix.
   • prompt is the description of the image you want generated.
   • output_folder is the S3 folder path structure.
   • file_name_prefix is the name of the image file which will end up with a Unix epoch time appended to it.
   • Example POST request BODY: {"prompt": "red porsche", "output_folder": "000/111", "file_name_prefix": "porsche_image"}
2. Four unique images will be generated based on your prompt and uploaded to the S3 bucket you specify in image_generation.py under AI_S3_BUCKET_NAME
3. You will get a JSON manifest of file names after the images are generated.
   • Example response: {"images": ["000/111/porsche_image-1728409676.376359.png", "000/111/porsche_image-1728409676.376369.png", "000/111/porsche_image-1728409676.376371.png", "000/111/porsche_image-1728409676.3763719.png"]}

Prerequisites for macOS laptop local development setup

• Amazon Web Services Account
• Enable Amazon Bedrock Access (Specifically Amazon Titan Image Generator v2) see: Manage access to Amazon Bedrock foundation models
• AWS CLI installed
• AWS CLI IAM user with Full Amazon Bedrock Access and Full Amazon S3 Access
• Verified on Python 3.10, 3.11, 3.12
• Anaconda or Miniconda installed
• AWS Default Region is set to us-east-1 you can change the region in the image_generation.py file under region_name='us-east-1'
• AWS S3 bucket

Prerequisites for EC2 Ubuntu instance setup

• Amazon Web Services Account
• Enable Amazon Bedrock Access (Specifically Amazon Titan Image Generator v2) see: Manage access to Amazon Bedrock foundation models
• EC2 Instance Role with AmazonBedrockFullAccess and AmazonS3FullAccess (note you can make this more secure by making a custom policy)
• Verified on EC2 Instance Ubuntu 22.04 and Ubuntu 24.04
• Verified on Python 3.10, 3.11, 3.12
• Virtualenv
• AWS Default Region is set to us-east-1 you can change the region in the image_generation.py file under region_name='us-east-1'
• AWS S3 bucket

AWS Resource Cost

As with most AWS services you will incur costs for usage.

• Pricing:
  • https://aws.amazon.com/bedrock/pricing/
  • https://aws.amazon.com/ec2/pricing/on-demand/

macOS laptop local development setup

conda create -n "ai-image-generator" python=3.11.0

conda activate ai-image-generator

git clone [email protected]:nethacker/ai-image-generator.git

cd ai-image-generator

pip install -r requirements.txt

Add your S3 bucket destination

vim image_generation.py

In image_generation.py change YOUR_S3_BUCKET_NAME_HERE at AI_S3_BUCKET_NAME to your chosen S3 bucket.

Run macOS laptop local development setup

To run AI Image Generator API Service

flask run --port 8080

You can reach the API at http://localhost:8080/.

From a command line you can test the API out by running:

curl -X POST --data '{"prompt": "red porsche", "output_folder": "000/111"}' http://127.0.0.1:8080/generate_images

You should see 4 new images in the S3 bucket you set.

EC2 Ubuntu instance setup steps

(This example assumes you have a ubuntu user with /home/ubuntu)

Install some dependencies

sudo apt -y update

sudo apt -y install build-essential openssl

sudo apt -y install libpq-dev libssl-dev libffi-dev zlib1g-dev

sudo apt -y install python3-pip python3-dev

sudo apt -y install nginx

sudo apt -y install virtualenvwrapper

Clone the GIT repository

cd /home/ubuntu

git clone https://github.com/nethacker/ai-image-generator.git

Setup the Python environment

virtualenv ai-image-generator_env

source ai-image-generator_env/bin/activate

Install the AI Image Generator API service package dependencies

cd /home/ubuntu/ai-image-generator

pip install -r requirements.txt

Add your S3 bucket destination

vim image_generation.py

In image_generation.py change YOUR_S3_BUCKET_NAME_HERE at AI_S3_BUCKET_NAME to your chosen S3 bucket.

Setup systemd to daemonize and bootstrap the AI Image Generator API service (Port 8080)

sudo cp systemd/ai-image-generator.service /etc/systemd/system/

sudo systemctl start ai-image-generator

sudo systemctl enable ai-image-generator.service

Install NGINX to help scale and handle connections (Port 80)

sudo cp nginx/nginx_ai-image-generator.conf /etc/nginx/sites-available/nginx_ai-image-generator.conf

sudo rm /etc/nginx/sites-enabled/default

sudo ln -s /etc/nginx/sites-available/nginx_ai-image-generator.conf /etc/nginx/sites-enabled

sudo systemctl restart nginx

You can reach the API at http://{yourhost}.

From a command line you can test the API out by running:

curl -X POST --data '{"prompt": "red porsche", "output_folder": "000/111", "file_name_prefix": "porsche_image"}' http://{yourhost}:80/generate_images

Notes

• Make sure to open up port 80 in your EC2 Security Group associated to the instance.
• For HTTPS (TLS) you can use AWS ALB or AWS CloudFront
• This AI Image Generator API service does not take into consideration security controls, that is your responsibility.
• Please read Amazon Bedrock FAQ's for general questions about AWS LLM resources used.