API
If you're looking for an API, you can choose from your desired programming language.
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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-samaritan_3d"
# Request payload
data = {
"prompt": "1 beautiful woman, office suit, coat, shirt, silver hair, hand Sui Ishida (best quality, masterpiece)",
"negative_prompt": "EasyNegativeV2 ng_deepnegative_v1_75t greyscale (worst quality, low quality)",
"scheduler": "euler_a",
"num_inference_steps": 20,
"guidance_scale": 7.5,
"samples": 1,
"seed": 692203384,
"img_width": 512,
"img_height": 768,
"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
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
Number of samples to generate.
min : 1,
max : 4
Seed for image generation.
Width of the image.
Allowed values:
Height of the Image
Allowed values:
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.
Samaritan
The Samaritan Model is a cutting-edge AI tool specifically designed for generating 3D cartoon images with ease. Tailored for artists, animators, and designers, this model stands out in its ability to effortlessly create vibrant and dynamic 3D cartoon visuals, making it a valuable asset in the realm of digital animation and graphic design. The model's ability to render cartoons in three dimensions adds depth and life to the images, enhancing their appeal and engagement.
Advantages
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3D Cartoon Rendering: Specializes in transforming 2D cartoon concepts into captivating 3D images.
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Creative Versatility: Offers a wide range of possibilities for cartoon styles and themes..
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High-Quality Outputs: Produces vibrant, detailed, and visually appealing cartoon images.
Use Cases
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Animation and Film:Ideal for animators and filmmakers looking to create unique 3D cartoon scenes and characters.
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Digital Art: Artists can explore new dimensions in cartoon artistry.
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Marketing and Advertising: Useful for creating engaging 3D cartoon visuals for promotional content.
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Educational Content: Enhances learning materials with appealing 3D cartoon illustrations.
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sdxl-controlnet
SDXL ControlNet gives unprecedented control over text-to-image generation. SDXL ControlNet models Introduces the concept of conditioning inputs, which provide additional information to guide the image generation process
sdxl-inpaint
This model is capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask
codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
