Stable Diffusion XL
A text to image foundation model that generates detailed images from short prompts. Learn more
Deploy Stable Diffusion XL behind an API endpoint in seconds.
Example usage
The model accepts a few main inputs:
prompt: This is text describing the image you want to generate. The output images tend to get better as you add more descriptive words to the prompt.width: Width of the image in pixels. Default is 1024.height: Height of the image in pixels. Default is 1024.
The output JSON object contains a key called data which represents the generated image as a base64 string.
1import requests
2import os
3import base64
4from PIL import Image
5from io import BytesIO
6
7# Replace the empty string with your model id below
8model_id = ""
9baseten_api_key = os.environ["BASETEN_API_KEY"]
10BASE64_PREAMBLE = "data:image/png;base64,"
11
12# Function used to convert a base64 string to a PIL image
13def b64_to_pil(b64_str):
14 return Image.open(BytesIO(base64.b64decode(b64_str.replace(BASE64_PREAMBLE, ""))))
15
16data = {
17 "prompt": "a little boy looking through a large magical portal, the boy sees a futuristic human civilization in that portal, extremely detailed, trending on artstation, 8k"
18}
19
20# Call model endpoint
21res = requests.post(
22 f"https://model-{model_id}.api.baseten.co/production/predict",
23 headers={"Authorization": f"Api-Key {baseten_api_key}"},
24 json=data
25)
26
27# Get output image
28res = res.json()
29output = res.get("data")
30
31# Convert the base64 model output to an image
32img = b64_to_pil(output)
33img.save("output_image.png")
34os.system("open output_image.png")1{
2 "status": "success",
3 "data": "iVBORw0KGgoAAAANSUhEUgAABAAAAAQACAIAAA...",
4 "time": 7.62
5}
You can also provide negative_prompt as in input to steer the model away from things you don't want to generate.
The num_inference_steps parameter is the number of iterations the model does before creating the final image.
The output JSON object contains a key called data which represents the generated image as a base64 string.
1import requests
2import os
3import base64
4from PIL import Image
5from io import BytesIO
6
7# Replace the empty string with your model id below
8model_id = ""
9baseten_api_key = os.environ["BASETEN_API_KEY"]
10BASE64_PREAMBLE = "data:image/png;base64,"
11
12# Function used to convert a base64 string to a PIL image
13def b64_to_pil(b64_str):
14 return Image.open(BytesIO(base64.b64decode(b64_str.replace(BASE64_PREAMBLE, ""))))
15
16data = {
17 "prompt": "Extremely detailed and intricate scene of baby phoenix hatchling cuddled up resting on a pile of ashes surrounded by fire and smoke, rays of sunlight shine on the phoenix, in the background is a dense dark forest, settings: f/8 aperture, full shot, hyper realistic, 4k",
18 "negative_prompt": "worst quality, low quality",
19 "width": 1248,
20 "height": 832,
21 "num_inference_steps": 35,
22 "guidance_scale": 14
23}
24
25# Call model endpoint
26res = requests.post(
27 f"https://model-{model_id}.api.baseten.co/production/predict",
28 headers={"Authorization": f"Api-Key {baseten_api_key}"},
29 json=data
30)
31
32# Get output image
33res = res.json()
34output = res.get("data")
35
36# Convert the base64 model output to an image
37img = b64_to_pil(output)
38img.save("output_image.png")
39os.system("open output_image.png")1{
2 "status": "success",
3 "data": "iVBORw0KGgoAAAANSUhEUgAABAAAAAQACAIAAA...",
4 "time": 7.62
5}
