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-manmarumix"
# Request payload
data = {
"prompt": "masterpiece,best quality, dog, 1girl, sitting,park,looking at viewer",
"negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, nswf",
"scheduler": "dpmpp_2m",
"num_inference_steps": 20,
"guidance_scale": 7,
"samples": 1,
"seed": 277487585,
"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.
Manmarumix
The Manmarumix model stands as a testament to the fusion of artistic expression and digital clarity. This innovative AI model is skillfully adjusted to balance the charm of a hand-drawn look with the distinctiveness of clear backgrounds, all while maintaining the unique characteristics of the Thumbelina Model.
At its core, the Manmarumix model is engineered to refine and enhance digital imagery. It delicately adjusts the hand-drawn elements to ensure they blend seamlessly with vividly clear backgrounds. This balance is achieved without compromising the distinctive style and charm inherent in the original Thumbelina Model
Advantages
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Artistic Balance: Perfectly blends hand-drawn aesthetics with clear, distinct backgrounds.
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Enhanced Clarity: Ensures backgrounds are crisp and detailed, adding depth to the overall image..
Use Cases
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Animation and Film:Offers a unique aesthetic for animated projects and digital storytelling.
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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 cartoon visuals for promotional content.
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Educational Content: Enhances learning materials with appealing cartoon illustrations.
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