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-cuterichstyle"
# Request payload
data = {
"prompt": "cbzbb , elon musk ,charachter ,cute, little, beautiful, devian art, trending artstation, digital art, detailed, cute, realistic, humanoide, character, tiny,cinematic sho ,cinematic lights,elon musk,looks happy",
"negative_prompt": "pencil draw, bad photo, bad draw , anime,ironman,robot,animals,explosions,text,letters,ugly",
"scheduler": "dpmpp_sde_ancestral",
"num_inference_steps": 20,
"guidance_scale": 7,
"samples": 1,
"seed": 630080558,
"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.
Cute Rich Style
Cute Rich Style model is based on Stable Diffusion 1.5 framework and trained on images from Midjourney, this model is a masterclass in generating lifelike representations of people, animals, and creatures. By integrating photos from Midjourney, the model achieves a unique blend of realism and creativity, ensuring outputs that are both captivating and true to life.
Advantages
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Diverse Image Generation: Whether it's people in varied attires and poses or animals and creatures from your wildest imaginations, Cure Rich Style delivers with precision.
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High-Quality Realism: Thanks to the integration of Midjourney photos, the model crafts images that resonate with lifelike accuracy.
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Versatile Applications: From human figures to the animal kingdom and beyond, the model's range is vast and varied.
Optimized for Detail: Every generated image boasts intricate details, capturing the essence of the subject with finesse.
Use Cases
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Character Design: Ideal for artists and game developers looking to design detailed human or creature characters.
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Film and Animation: Filmmakers and animators can harness the Cure Rich Style model for pre-visualization and character development.
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Digital Art Creation: Artists can craft detailed portraits, wildlife art, or mythical creatures with ease.
Other Popular Models
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sdxl1.0-txt2img
The SDXL model is the official upgrade to the v1.5 model. The model is released as open-source software
codeformer
CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.
sd2.1-faceswapper
Take a picture/gif and replace the face in it with a face of your choice. You only need one image of the desired face. No dataset, no training
