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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-realism-lightning"
# Request payload
data = {
"prompt": "A movie still of a fat old man laughing, glasses, suit and tie, white shirt",
"negative_prompt": "((close up)),(octane render, render, drawing, 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",
"samples": 1,
"scheduler": "DPM++ SDE",
"num_inference_steps": 10,
"guidance_scale": 1.4,
"seed": 98556327,
"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
blur, noisy, 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.
Realism Lightning SDXL
The Realism Lightning SDXL model represents a breakthrough in image generation technology, specializing in producing skin realism and anatomically accurate images with a focus on enhancing the realism of features like the eyes. This advanced model is engineered to deliver exceptional quality and precision in creating lifelike visual representations.Distinguished by its emphasis on skin realism, anatomical accuracy, and eye detail, the Realism Lightning SDXL sets a new standard for excellence in generating images that closely mimic real-life characteristics.
A standout feature of the Realism Lightning SDXL is its exceptional ability to swiftly and efficiently create high-quality images that prioritize skin realism and anatomical precision, making it an ideal choice for applications that require lifelike visual content. With the capability to enhance features like eyes and skin texture with unparalleled accuracy, this model offers a unique blend of speed and anatomical fidelity.
To optimize the performance of the Realism Lightning SDXL, ensuring compatibility with the DPM++ SDE Karras / DPM++ SDE sampler is crucial. Utilizing 4-6 sampling steps along with a CFG Scale set between 1 and 2 is recommended to achieve peak performance and efficiency in the image generation process. These tailored settings are essential for unlocking the full potential of this advanced model and producing images that exhibit exceptional realism and detail.
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