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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/sd1.5-controlnet-depth"
# Request payload
data = {
"image": image_url_to_base64("https://segmind.com/depth.jpeg"), # Or use image_file_to_base64("IMAGE_PATH")
"samples": 1,
"prompt": "young african american man, black suit, smiling, white background",
"negative_prompt": "mangled ears, Disfigured, cartoon, blurry, nude",
"scheduler": "UniPC",
"num_inference_steps": 25,
"guidance_scale": 7.5,
"strength": 1,
"seed": 9715432854,
"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
Number of samples to generate.
min : 1,
max : 4
Prompt to render
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 0.1,
max : 25
How much to transform the reference image
min : 0.1,
max : 1
Seed for image generation.
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.
ControlNet Depth
ControlNet Depth is a neural network that can be used to control the output of Stable Diffusion models with depth information. This allows you to specify specific features that you want to include in the output image, such as the overall structure of the image, the pose of the subject, or the style of the image, as well as the depth information.
To use ControlNet Depth, you can follow these steps:
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Go to the Segmind website: https://www.segmind.com/ and sign up for a free account.
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Click on the "Models" tab and select "ControlNet Depth".
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Click on the "Try it out" button and upload an image that you want to control.
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Click on the "Generate" button to generate the controlled image.
ControlNet Depth is a powerful tool that can be used for various purposes. It is still under development, but it has the potential to revolutionize the way we interact with images. Reach out to us to learn more about how we can help you with customized solutions, large-scale cost-effective deployment, and other use cases.
Applications/Use Cases
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Creating images with specific features: ControlNet Depth can be used to create images with specific features, such as a particular pose, a specific style, or a specific object, as well as depth information. This can be useful for creating images for creative projects or for research purposes.
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Improving the quality of images: ControlNet Depth can be used to improve the quality of images by removing noise or by adding detail. This can be useful for restoring damaged images or creating more realistic ones.
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Controlling the output of Stable Diffusion models with depth information: ControlNet Depth can be used to control the output of Stable Diffusion models to include specific features, as well as depth information. This can be useful for creating images with a particular style or for creating images consistent with a particular dataset.
Popular Models
