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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/flux-realism-lora"
# Request payload
data = {
"prompt": "a young woman smiling while speaking onstage from segmind, white background with corporate logos blurred out, tech conference",
"steps": 20,
"seed": 6652105,
"scheduler": "simple",
"sampler_name": "euler",
"aspect_ratio": "2:3",
"width": 1024,
"height": 1024,
"upscale_value": 2,
"lora_strength": 0.8,
"samples": 1,
"upscale": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Text prompt for generating the image
Number of steps for generating the image
min : 1,
max : 100
Seed for random number generation
Scheduler type for image generation
Allowed values:
Sampler type for image generation
Allowed values:
Aspect ratio for the generated image
Allowed values:
To enable custom image width, choose 'null' in the aspect ratio option.
min : 64,
max : 4096
To enable custom image height, choose 'null' in the aspect ratio option.
min : 64,
max : 4096
Value by which to upscale the image
min : 1,
max : 3
Strength of the LoRA (Low-Rank Adaptation) for fine-tuning
min : -10,
max : 10
Number of samples to generate
min : 1,
max : 4
Whether to upscale the image or not
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.
Flux Realism Lora
Flux Realism Lora, developed by XLabs AI is a cutting-edge model designed to generate realistic images from textual descriptions. Whether you’re an artist, marketer, or developer, understanding how to use this powerful tool can unlock a world of creative possibilities.
Model Information
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Architecture: FLUX Realism Lora leverages deep neural networks to interpret natural language prompts and create corresponding images.
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Fine-Tuning: The model allows fine-tuning with adjustable parameters for customized results.
How to Use the Model
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Input Prompt: Start by providing a descriptive text prompt. Be precise and concise.
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Fine-Tuning Parameters:
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Steps: Adjust the number of iterations (higher steps for more refined results).
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Guidance Scale: Control fidelity to the prompt (higher values for closer adherence).
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Scheduler Type: Choose from different algorithms for parameter evolution.
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Seed Value: Ensure reproducibility.
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Upscale Option: Enhance resolution post-generation.
- Generate : Run the model to create your image.
Use Cases
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Digital Art: Create stunning visuals for illustrations, posters, and digital media.
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Marketing: Generate eye-catching content for campaigns.
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Education: Illustrate concepts and ideas.
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Concept Art: Ideal for gaming or film industry pre-visualization.
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