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/dminhk-dog-example-sdxl-lora"
# Request payload
data = {
"prompt": "cute puppy dog excited",
"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.
Dog Example SDXL LoRA
The Dog Example SDXL LoRA, a specialized AI model within the Stable Diffusion XL framework, uniquely trained to enhance canine imagery. This model is based on LoRA adaptation weights, specifically trained using DreamBooth on a photograph of a dog. This training approach ensures the model's proficiency in accurately rendering canine features, textures, and expressions
Advantages
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Canine-Focused Imagery: Excellently captures the essence and details of dogs in images.
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High Precision:Trained on dog photos for enhanced accuracy in canine features.
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Versatile Applications: Suitable for various uses, from pet photography enhancement to creative dog-themed art.
Use Cases
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Pet Photography:Enhance the quality and detail of dog photographs.
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Veterinary Education: Create detailed canine images for educational purposes.
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Pet Care Industry: Ideal for creating visuals for pet care products and services.
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Advertising and Marketing: Use in campaigns or materials featuring dogs.
Other Popular Models
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sdxl-inpaint
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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
