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/Norod78-SDXL-StickerSheet-Lora"
# Request payload
data = {
"prompt": "Cute sparkle pink barbie StickerSheet, Very detailed, clean, high quality, sharp image, Eric Wallis",
"negative_prompt": "boring, poorly drawn, bad artist, (worst quality:1.4), simple background, uninspired, (bad quality:1.4), monochrome, low background contrast, background noise, duplicate, crowded, (nipples:1.2), big breasts",
"scheduler": "UniPC",
"num_inference_steps": 25,
"guidance_scale": 8,
"samples": 1,
"seed": 3426017487,
"img_width": 1024,
"img_height": 1024,
"base64": False,
"lora_scale": 1
}
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.
Scale of the lora
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.
SDXL StickerSheet LoRA
SDXL StickerSheet LoRA harnesses the capabilities of the SDXL 1.0 model, it's expertly fine-tuned on a comprehensive collection of sticker images, enabling it to produce a wide variety of sticker designs that cater to different themes, styles, and preferences.
Advantages
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Diverse Sticker Generation: Creates a wide array of sticker designs, from cute and whimsical to sleek and professional.
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High-Quality Outputs:Ensures crisp, clear, and vibrant stickers, perfect for digital and print use.
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Creative Flexibility: Offers endless possibilities for customizing sticker sheets according to specific needs or themes.
Use Cases
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Graphic Design:Design unique stickers for branding, marketing materials, or digital platforms.
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Personal Projects: Create custom stickers for journals, scrapbooking, or personal collections.
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E-commerce: Design sticker packs for online stores, catering to the growing demand for digital stickers.
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Social Media: Generate eye-catching stickers for social media posts, stories, or messaging apps.
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