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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/sdxl-openpose"
# Request payload
data = {
"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")
"prompt": "A ballerina dancing on stage with two legs",
"negative_prompt": "low quality, ugly, painting",
"samples": 1,
"scheduler": "Euler a",
"num_inference_steps": 30,
"guidance_scale": 7.5,
"seed": 65312568548,
"controlnet_scale": 0.5,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Input Image
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
Base64 encoding of the output 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.
SDXL Open Pose
SDXL OpenPose Model, a groundbreaking AI tool that redefines human pose estimation. This model synergizes the robust control features of ControlNet with the precision of OpenPose, offering an unparalleled level of accuracy and control in human pose analysis within the Stable Diffusion framework.
The SDXL OpenPose Model is engineered with a sophisticated blend of ControlNet's control mechanisms and OpenPose's advanced pose estimation algorithms. This powerful combination processes visual data with remarkable accuracy, enabling real-time detection and manipulation of human poses.
Advantages
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Pose Manipulation: Allows for real-time adjustments, offering immediate control over human poses.
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Broad Application Spectrum: Adaptable across various industries, from entertainment to health, due to its extensive capabilities.
Use Cases
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Animation and Film Production:Enables animators to craft realistic human movements and postures, adding authenticity to animated features.
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Gaming: Enhances gaming experiences with more natural character movements and in-game interactions.
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Immersive VR and AR: Improves the realism of AR and VR by accurately translating real-world movements to digital avatars.
Popular Models
