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https://github.com/bentoml/BentoDiffusion
BentoDiffusion: A collection of diffusion models served with BentoML
https://github.com/bentoml/BentoDiffusion
ai diffusion-models fine-tuning kubernetes lora model-serving stable-diffusion
Last synced: 4 days ago
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BentoDiffusion: A collection of diffusion models served with BentoML
- Host: GitHub
- URL: https://github.com/bentoml/BentoDiffusion
- Owner: bentoml
- License: apache-2.0
- Created: 2023-06-12T19:33:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-01T01:42:19.000Z (about 2 months ago)
- Last Synced: 2024-12-09T17:08:39.569Z (13 days ago)
- Topics: ai, diffusion-models, fine-tuning, kubernetes, lora, model-serving, stable-diffusion
- Language: Python
- Homepage: https://bentoml.com
- Size: 7.55 MB
- Stars: 340
- Watchers: 12
- Forks: 25
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Self-host Diffusion Models with BentoML
This repository contains a series of BentoML example projects, demonstrating how to deploy different models in [the Stable Diffusion (SD) family](https://huggingface.co/models?other=stable-diffusion), which is specialized in generating and manipulating images or video clips based on text prompts.
See [here](https://github.com/bentoml/BentoML/tree/main/examples) for a full list of BentoML example projects.
The following guide uses SDXL Turbo as an example.
## Prerequisites
If you want to test the Service locally, we recommend you use a Nvidia GPU with at least 12GB VRAM.
## Install dependencies
```bash
git clone https://github.com/bentoml/BentoDiffusion.git
cd BentoDiffusion/sdxl-turbo
# Recommend Python 3.11
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.
```python
$ 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](http://localhost:3000/). You can interact with it using the Swagger UI or in other different ways.
CURL
```bash
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
```python
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](https://docs.bentoml.com/en/latest/use-cases/diffusion-models/sdxl-turbo.html).
## Deploy to BentoCloud
After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. [Sign up](https://www.bentoml.com/) if you haven't got a BentoCloud account.
Make sure you have [logged in to BentoCloud](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html), then run the following command to deploy it.
```bash
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](https://docs.bentoml.com/en/latest/guides/containerization.html).
## Choose another diffusion model
To deploy a different diffusion model, go to the corresponding subdirectories of this repository.
- [ControlNet](controlnet/)
- [Latent Consistency Model](lcm/)
- [Stable Diffusion 2 with 4x upscaler](sd2upscaler/)
- [SDXL Lightning](sdxl-lightning/)
- [SDXL Turbo](sdxl-turbo/)
- [Stable Video Diffusion](svd/)