bentoml / bentosdxlturbo

how to build an image generation application using BentoML

Serving SDXL Turbo with BentoML

Stable Diffusion XL Turbo is a real-time text-to-image generation model utilizing a novel distillation technique called Adversarial Diffusion Distillation (ADD). This technology enables SDXL Turbo to generate images in a single step, significantly enhancing performance and reducing computational requirements without sacrificing image quality.

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

Prerequisites

• You have installed Python 3.8+ 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.
• To run the Service locally, you need a Nvidia GPU with at least 12G VRAM.

Install dependencies

git clone https://github.com/bentoml/BentoSDXLTurbo.git
cd BentoSDXLTurbo
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 .

2024-01-18T18:31:49+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:SDXLTurboService" listening on http://localhost:3000 (Press CTRL+C to quit)
Loading pipeline components...: 100%

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/txt2img' \
  -H 'accept: image/*' \
  -H 'Content-Type: application/json' \
  -d '{
  "prompt": "A cinematic shot of a baby racoon wearing an intricate italian priest robe.",
  "num_inference_steps": 1,
  "guidance_scale": 0
}'

Python client

import bentoml

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
        result = client.txt2img(
            prompt="A cinematic shot of a baby racoon wearing an intricate italian priest robe.",
            num_inference_steps=1,
            guidance_scale=0.0
        )

For detailed explanations of the Service code, see Stable Diffusion XL Turbo.

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.