This is a BentoML example project, showing you how to serve and deploy a series of diffusion models in the Stable Diffusion (SD) family, which is specialized in generating and manipulating images based on text prompts.

See here for a full list of BentoML example projects.

The following guide uses SDXL Turbo as an example.

• 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.
• If you want to test the Service locally, a Nvidia GPU with at least 12GB VRAM will boost performance significantly.
• (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

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

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.

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.

To deploy a different diffusion model, go to the corresponding subdirectories of this repository.

• ControlNet
• Latent Consistency Model
• Stable Diffusion 2 with 4x upscaler
• SDXL Lightning
• SDXL Turbo
• Stable Video Diffusion