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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/sdxl1.0-realvis-lightning"
# Request payload
data = {
"prompt": "mountains, snow, fog, volumetric, closeup portrait photo of young woman in dark clothes, perfect detailed eyes, natural skin, film grain, kayrissnovem, grey eyes",
"negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth",
"samples": 1,
"scheduler": "DPM++ SDE",
"num_inference_steps": 8,
"guidance_scale": 1,
"seed": 968875,
"img_width": 1024,
"img_height": 1024,
"base64": False
}
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
(octane render, render, drawing, anime, bad photo, bad photography:1.3), (worst quality, low quality, blurry:1.2), (bad teeth, deformed teeth, deformed lips), (bad anatomy, bad proportions:1.1), (deformed iris, deformed pupils), (deformed eyes, bad eyes), (deformed face, ugly face, bad face), (deformed hands, bad hands, fused fingers), morbid, mutilated, mutation, disfigured
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 1,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
Seed for image generation.
min : -1,
max : 999999999999999
Can only be 1024 for SDXL
Allowed values:
Can only be 1024 for SDXL
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.
Realvis Lightning SDXL
The Realvis Lightning SDXL model is an advanced solution designed for swift and efficient generation of high-quality, photorealistic images. This model, an enhanced iteration of the Realvis XL, excels in delivering exceptional photorealistic images with remarkable speed. Its standout feature lies in its capacity to produce top-tier 1024px images rapidly, catering to applications that necessitate quick image generation.
To maximize the Realvis Lightning SDXL model's performance, it is crucial to ensure compatibility with the DPM++ SDE Karras / DPM++ SDE sampler. Optimal utilization involves employing 4-6 sampling steps and a CFG Scale of 1-2. These specific settings are key to achieving an effective and efficient image generation process, enabling users to fully leverage the capabilities of this sophisticated model.
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