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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-icbinp"
# Request payload
data = {
"prompt": "A red vintage car on the streets of New York, front view, car in the center of the road, hyper quality, intricate detail, masterpiece, photorealistic, ultra realistic, maximum detail, foreground focus, instagram, 8k, volumetric light, cinematic, octane render, uplight, no blur, 8k",
"negative_prompt": "((Semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4)), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate",
"scheduler": "dpmpp_2m",
"num_inference_steps": 30,
"guidance_scale": 10,
"samples": 1,
"seed": 61150574526948,
"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.
ICBINP is short for "I can't believe it's not photography"engineered on Stable Diffusion 1.5, that prioritizes hyper-realism. It pushes the boundaries of digital imagery, crafting portraits so lifelike that they challenge the discerning eye. With its prowess in capturing the intricate details of hair, skin, and eyes, and its enhanced capability for night and dark imagery.
Digital Photography: Ideal for professionals and enthusiasts looking to create lifelike portraits without the need for a camera.
Film and Animation: Filmmakers and animators can harness ICBINP for character design and scene creation, ensuring photorealistic outputs.
Advertising and Marketing: Marketers can create compelling visuals for campaigns, ensuring high audience engagement with lifelike images.
Art and Design: Artists can craft detailed portraits, enhancing their artworks with hyper-realistic human features.