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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-schnell"
# Request payload
data = {
"prompt": "a bengal tiger in an astronaut suit on mars, cubist style holding a sign saying 'awesome text gen'",
"steps": 4,
"seed": 123456789,
"sampler_name": "euler",
"scheduler": "normal",
"samples": 1,
"width": 1024,
"height": 1024,
"denoise": 1
}
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 inference steps for image generation
min : 1,
max : 8
Seed for random number generation
Sampler for the image generation process
Allowed values:
Scheduler for the image generation process
Allowed values:
Number of samples to generate
Image height can be between 512 and 2048 in multiples of 8
Image height can be between 512 and 2048 in multiples of 8
Denoise level for the generated image
min : 0,
max : 1
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.1 Schnell
Flux Schnell by Black Forest Labs is a state-of-the-art text-to-image generation model engineered for speed and efficiency. Utilizing a streamlined architecture, Flux Schnell combines advanced AI techniques with optimized processing capabilities to produce high-quality images rapidly. It is designed to meet the demands of users requiring quick turnaround times without compromising on output quality.
How to Use the Model
To use the Flux Schnell model:
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Input Text Prompt: Provide a textual description of the desired image. The model processes this input to generate a corresponding visual output.
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Run the Model: Execute the model with your text input. The AI algorithm interprets the description to produce an image.
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Review Outputs: Evaluate the generated images for quality and relevance to your input.
Use Cases
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Graphic Design: Automate the creation of graphics based on simple text descriptions, saving time on repetitive design tasks.
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Advertising: Generate visual content tailored to marketing campaigns, quickly producing assets that align with brand messages.
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Content Creation: Assist writers and content creators in visualizing their narratives by generating illustrative images from textual descriptions.
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Web Development: Enhance websites with unique, dynamically generated images that improve user engagement and aesthetic appeal.
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Research and Development: Utilize the model for experimental purposes in AI research, testing the boundaries of text-to-image generation capabilities.
Popular Models
