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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/ip-sdxl-openpose"
# Request payload
data = {
"prompt": "ballerina",
"negative_prompt": "lowquality, badquality, sketches",
"samples": 1,
"scheduler": "Euler a",
"num_inference_steps": 25,
"guidance_scale": 7,
"seed": 354849415,
"controlnet_scale": 0.5,
"ip_adapter_scale": 0.5,
"strength": 0.9,
"base64": False,
"input_image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/outputs/sdxl_input_openpose.jpg"), # Or use image_file_to_base64("IMAGE_PATH")
"ip_image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/outputs/pose_ip_input.jpeg") # Or use image_file_to_base64("IMAGE_PATH")
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
Seed for image generation.
min : -1,
max : 999999999999999
Scale for classifier-free guidance
min : 0,
max : 1
Scale for classifier-free guidance
min : 0,
max : 1
Scale for classifier-free guidance
min : 0,
max : 1
Base64 encoding of the output image.
Input Image
Ip Image
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
IP Adapter Openpose XL
IP Adapter Openpose XL is an innovative AI model that redefines the capabilities of the SDXL framework, especially in the realm of human pose and gesture analysis. This model is a fusion of the IP Adapter and the Openpose Preprocessor, designed to create images that are not only visually rich but also contextually detailed, particularly in scenarios involving human subjects.
At the heart of IP Adapter Openpose XL is the integration of two advanced components. The IP Adapter empowers the SDXL model to effectively combine image and text prompts, while the Openpose Preprocessor excels in analyzing and identifying human poses and gestures. This combination ensures accurate interpretation and representation of human figures and their movements in the generated images.
Key Features:
-
Dual Prompt Integration : Seamlessly combines image and text prompts for intricate image transformations.
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Human Pose Analysis:Utilizes the Openpose Preprocessor for precise interpretation of human poses and gestures.
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Accurate Human Figure Interpretation:: Ensures faithful representation of human subjects and their movements.
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Creative Versatility: Ideal for a wide range of applications, from digital art to scientific visualization.
Applications:
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Digital Art and Illustration: Create complex artworks that blend elements from different images and concepts.
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Animation and Film Production: Enhance character design and animation with precise human figure interpretation.
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Photography Enhancement: Combine photographic elements with artistic prompts for unique compositions.
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Fashion and Design: Visualize fashion designs and concepts with accurately posed human figures.
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Personal Projects: Experiment with blending personal photos with creative text prompts for unique creations.
Popular Models
