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-colorful"
# Request payload
data = {
"prompt": "((( splash of colorful paint))) Colorful, beautiful cat lady, dark, splash, disembodied head, Black ink flow, photorealistic, intricately detailed, fluid gouache, calligraphy, acrylic, watercolor art, 8k concept art, intricately detailed, complex, elegant, expansive, fantastical, (style-paintmagic),(style of Kim Keever:1.2), (cat), disembodied head, photorealistic, intricately detailed, 8k concept art, intricately detailed, complex, elegant, expansive, fantastical",
"negative_prompt": "(low quality:1.4), (worst quality:1.4), (monochrome:1.1), normal quality, cropped, fingers, deformed, distorted, disfigured, limb, hands, anatomy, long neck, negative_hand-neg, skin blemishes, flowers",
"scheduler": "dpmpp_sde_ancestral",
"num_inference_steps": 25,
"guidance_scale": 9,
"samples": 1,
"seed": 573528313,
"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.
Colorful
Stable Diffusion Colorful AI model is a latent diffusion model that can be used to generate images from text prompts. Stable Diffusion Colorful AI model is a powerful tool for AI developers who want to experiment with creative text-to-image generation for images that are colorful and vibrant. It is easy to use, and it can be used to generate images in a variety of styles.
Applications/Use Cases
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Generating concept art for cartoons and animated movies.
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Creating marketing materials, such as product images and social media graphics.
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Designing merchandise for children's products.
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