Serving SDXL and ControlNet with BentoML

ControlNet is a model designed to control image diffusion processes by conditioning them with additional input images, such as canny edges, user sketches, human poses, depth maps, and more. This allows for greater control over image generation by guiding the model with specific inputs, making it easier to generate targeted images.

This is a BentoML example project, demonstrating how to build an image generation inference API server, using the SDXL model and the ControlNet model. See here for a full list of BentoML example projects.

Prerequisites

• You have installed Python 3.9+ and pip. See the Python downloads page to learn more.
• You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
• (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

Install dependencies

git clone https://github.com/bentoml/BentoControlNet.git
cd BentoControlNet
pip install -r requirements.txt

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

$ bentoml serve .

2024-01-18T09:43:40+0800 [INFO] [cli] Prometheus metrics for HTTP BentoServer from "service:APIService" can be accessed at http://localhost:3000/metrics.
2024-01-18T09:43:41+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:APIService" listening on http://localhost:3000 (Press CTRL+C to quit)

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl -X 'POST' \
    'http://localhost:3000/generate' \
    -H 'accept: image/*' \
    -H 'Content-Type: multipart/form-data' \
    -F 'image=@example-image.png;type=image/png' \
	-F 'prompt=A young man walking in a park, wearing jeans.' \
	-F 'negative_prompt=ugly, disfigured, ill-structured, low resolution' \
	-F 'controlnet_conditioning_scale=0.5' \
	-F 'num_inference_steps=25'

Python client

import bentoml
from pathlib import Path

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
    result = client.generate(
        image=Path("example-image.png"),
        prompt="A young man walking in a park, wearing jeans.",
        negative_prompt="ugly, disfigured, ill-structure, low resolution",
        controlnet_conditioning_scale=0.5,
        num_inference_steps=25,
    )

For detailed explanations of the Service code, see ControlNet.

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.