Images (Experimental)#
Learn how to generate images with Xinference.
Introduction#
The Images API provides two methods for interacting with images:
The Text-to-image endpoint create images from scratch based on a text prompt.
The Image-to-image endpoint allows you to generate a variation of a given image.
API ENDPOINT |
OpenAI-compatible ENDPOINT |
|---|---|
Text-to-Image API |
/v1/images/generations |
Image-to-image API |
/v1/images/variations |
Supported models#
The Text-to-image API is supported with the following models in Xinference:
sd-turbo
sdxl-turbo
stable-diffusion-v1.5
stable-diffusion-xl-base-1.0
sd3-medium
Quickstart#
Text-to-image#
The Text-to-image API mimics OpenAI’s create images API. We can try Text-to-image API out either via cURL, OpenAI Client, or Xinference’s python client:
curl -X 'POST' \
'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/images/generations' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "<MODEL_UID>",
"prompt": "an apple",
}'
import openai
client = openai.Client(
api_key="cannot be empty",
base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
client.images.generate(
model=<MODEL_UID>,
prompt="an apple"
)
from xinference.client import Client
client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")
model = client.get_model("<MODEL_UID>")
input_text = "an apple"
model.text_to_image(input_text)
{
"created": 1697536913,
"data": [
{
"url": "/home/admin/.xinference/image/605d2f545ac74142b8031455af31ee33.jpg",
"b64_json": null
}
]
}
Note
If you are running sd3-medium on a GPU less than 24GB and encountering out of memory,
consider to add an extra param for launching according to this article.
xinference launch --model-name sd3-medium --model-type image --text_encoder_3 None
Image-to-image#
You can find more examples of Images API in the tutorial notebook:
Learn from a Stable Diffusion ControlNet example