The image generations endpoint allows you to create an original image given a text prompt.
Generate an image
from openai import OpenAI
# Load environment variables
be_api_key= "BE_API_KEY"
base_url='http://0.0.0.0:1234/v1'
model="be-stable-diffusion-xl-base-1.0"
client = OpenAI(
base_url=base_url,
api_key=be_api_key
)
# We repurpose "n" as the seed for SDXL
response = client.images.generate(
model=model,
prompt="a photorealistic(1.5) image of a cat playing with a (ball)0.5 in a (forest)1.0",
size="512x512",
quality="standard",
n=2,
response_format="url" # This field is optional, the default value is url
)
image_url = response.data[0].url
Example Stable Diffusion XL generations
PROMPT
GENERATION
Image Edits
Also known as "inpainting", the image edits endpoint allows you to edit or extend an image by uploading an image indicating which areas should be replaced.
Image edits
from openai import OpenAI
# Load environment variables
be_api_key= "BE_API_KEY"
base_url='https://api.blockentropy.ai/v1'
model="be-stable-diffusion-xl-base-1.0"
client = OpenAI(
base_url=base_url,
api_key=be_api_key
)
# We repurpose "n" as the seed for SDXL
response = client.images.edit(
model=model,
prompt="remove dog",
size="512x512",
image=Path("img.png"),
n=1,
response_format="url" # This field is optional, the default value is url
)
image_url = response.data[0].url
IMAGE
MASK
OUTPUT
Prompt: a sunlit indoor lounge area with a pool containing a flamingo
The uploaded image and mask must both be square PNG images less than 4MB in size, and also must have the same dimensions as each other. The non-transparent areas of the mask are not used when generating the output, so they don’t necessarily need to match the original image like the example above.
Controlnet with open pose
Generate a new image using the same pose as the reference image provided and a prompt.
Controlnet with open pose
from openai import OpenAI
# Load environment variables
be_api_key= "BE_API_KEY"
base_url='https://api.blockentropy.ai/v1'
model="be-stable-diffusion-xl-base-1.0"
client = OpenAI(
base_url=base_url,
api_key=be_api_key
)
# We repurpose "n" as the seed for SDXL
response = client.images.edit(
model=model,
prompt="Darth vader dancing in the street",
size="512x512",
image=Path("person.png"),
n=1,
response_format="url", # This field is optional, the default value is url
user="cn" # This parameter is required to be set to "cn" to use controlnet
)
image_url = response.data[0].url_image
pose_url = response.data[0].url_pose
IMAGE
OUTPUT
POSE IMAGE
Prompt: "A ballerina standing in the street, high quality and photorealistic"
