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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/consistent-character-ai-neolemon"
# Request payload
data = {
"prompt": "((masterpiece, best quality, highly detailed)), simple background, character sheet, multiple poses, multiple actions, visible face, (one person), portrait, full body, young african american girl, white sweater, jeans, short curly brown hair, 2D illustration style",
"negative_prompt": "text, watermark, underexposed, ugly, jpeg, (worst quality, low quality, lowres, low details, oversaturated, undersaturated, overexposed, grayscale, bw, bad photo, bad art:1.4), (font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), cropped, out of frame, cut off, jpeg artifacts, out of focus, glitch, duplicate, (amateur:1.3), merging, clipping, (nsfw), multiple hands, mutant, glitch, uncanny, cross eye, broken face, astronaut, helmet, blurry,",
"image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/display_images/testing_imgs/ref.pose.png"), # Or use image_file_to_base64("IMAGE_PATH")
"num_inference_steps": 20,
"guidance_scale": 7,
"seed": 4898558797,
"samples": 1,
"strength": 0.8,
"scheduler": "karras",
"sampler": "dpmpp_2m",
"upscale_by": 2,
"upscale_steps": 20,
"upscale_guidance_scale": 7,
"upscale_scheduler": "karras",
"upscale_sampler": "dpmpp_2m",
"upscale_mode_type": "Linear",
"fd_steps": 20,
"fd_guidance_scale": 7,
"fd_scheduler": "karras",
"fd_sampler": "dpmpp_2m"
}
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 image generation
Negative prompt to avoid certain elements
URL of the input image
Number of steps for inference
min : 1,
max : 100
Guidance scale for image generation
min : 1,
max : 20
Seed for random number generation
Number of samples to generate
Strength of image generation
min : 0,
max : 1
Scheduler type for image generation
Allowed values:
Sampler type for image generation
Allowed values:
Upscale factor for the image
min : 1,
max : 4
Number of steps for upscaling
min : 1,
max : 100
Guidance scale for upscaling
min : 1,
max : 20
Scheduler type for upscaling
Allowed values:
Sampler type for upscaling
Allowed values:
Mode type for upscaling
Allowed values:
Number of steps for face detection
min : 1,
max : 100
Guidance scale for face detection
min : 1,
max : 20
Scheduler type for face detection
Allowed values:
Sampler type for face detection
Allowed values:
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.
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