bentoml / BentoSVD

Serving Stable Video Diffusion with BentoML

Stable Video Diffusion (SVD) is a foundation model for generative video based on the image model Stable Diffusion. It comes in the form of two primary image-to-video models, SVD and SVD-XT, capable of generating 14 and 25 frames at customizable frame rates between 3 and 30 frames per second.

This is a BentoML example project, demonstrating how to build a video generation inference API server, using the SVD 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.
• (Optional) To run this project locally, a Nvidia GPU with 16G+ VRAM is required.

Install dependencies

git clone https://github.com/bentoml/BentoSVD.git
cd BentoSVD
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. Skip to cloud deployment if you don't have a Nvidia GPU locally.

$ bentoml serve .

2024-01-19T07:29:04+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:SVDService" listening on http://localhost:3000 (Press CTRL+C to quit)
Loading pipeline components...: 100%

The server is now active. Open your browser at http://localhost:3000 to interact via the web UI, or use an HTTP API client to call the local endpoint:

CURL

curl -X 'POST' \
  'http://localhost:3000/generate' \
  -H 'accept: */*' \
  -H 'Content-Type: multipart/form-data' \
  -F 'image=@assets/sample.png;type=image/png' \
  -F 'decode_chunk_size=2' \
  -F 'seed=null' \
  -o generated.mp4

Python client

import bentoml
from pathlib import Path

with bentoml.SyncHTTPClient("http://localhost:3000/") as client:
    result = client.generate(
        decode_chunk_size=2,
        image=@assets/sample.png,
        seed=0,
    )

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.