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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-fantassifiedicons"
# Request payload
data = {
"prompt": "a magical sailor moon themed shield intricate, pastel color scheme, high quality, 8k, highly detailed",
"negative_prompt": "nil",
"scheduler": "dpmpp_2m",
"num_inference_steps": 30,
"guidance_scale": 10,
"samples": 1,
"seed": 95722011,
"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 image
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.
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Fantassified Icons model is built on the Stable Diffusion 1.5 framework, this model specializes in crafting icons that transport users to realms of magic and adventure, all set against minimalist backgrounds. The model is optimized for generating icons that resonate with the essence of fantasy games. Its design principles prioritize simplicity and clarity, ensuring that each icon stands out, especially when set against its signature plain backgrounds.
Fantasy-Focused Generation: The model excels in crafting icons reminiscent of fantasy games, from mystical artifacts to warrior gear.
Minimalist Backgrounds: Each icon is accentuated by a mostly plain background, ensuring focus and clarity.
Optimized for Simplicity: While the model thrives with straightforward designs like shields and potions, it may falter with non-fantasy or complex items.
Prompt Positioning: For those seeking artist styles, placing such prompts at the beginning yields better results.
User-Centric Design: Tailored to meet the needs of game developers and fantasy enthusiasts, the model offers an intuitive platform for icon generation.
Game Development: Ideal for game designers looking to populate their fantasy games with authentic and captivating icons.
Digital Art Creation: Artists can craft detailed fantasy-themed icons for various digital platforms.
Merchandise Design: Businesses can harness the model for designing merchandise like T-shirts, mugs, and posters with fantasy icons.
Interactive Design: App developers and UI/UX designers can integrate fantasy icons to enhance user engagement.
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