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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-zavychroma"
# Request payload
data = {
"prompt": "An astronaut riding a rainbow unicorn, cinematic, dramatic",
"negative_prompt": "(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation. tattoo, watermark, text, anime, illustration, sketch, 3d, vector art, cartoon, painting",
"samples": 1,
"scheduler": "Euler a",
"num_inference_steps": 35,
"guidance_scale": 7,
"seed": 5645189598,
"img_width": 896,
"img_height": 1152,
"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
Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
Number of samples to generate.
min : 1,
max : 4
Type of scheduler.
Allowed values:
Number of denoising steps.
min : 20,
max : 100
Scale for classifier-free guidance
min : 1,
max : 25
Seed for image generation.
min : -1,
max : 999999999999999
Image width can be between 512 and 2048 in multiples of 8
Image height can be between 512 and 2048 in multiples of 8
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.
Zavychroma SDXL
Zavychroma SDXL emerges as the natural successor to the ZavyMix SD1.5 model, now reimagined for the SDXL framework. This model line is a testament to the seamless integration of magic and realism, a signature style that ZavyMix fans have come to love. With its transition to SDXL, Zavychroma SDXL promises not only a continuation of this unique aesthetic but also significant enhancements in image quality and coherence, especially in intricate details like eyes and teeth.
Advantages
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Enhanced Realism: Improved rendering of realistic details, particularly in complex areas like eyes and teeth.
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Refiner-Free Excellence: Delivers high-quality results without relying on a refiner, streamlining the creative process.
Use Cases
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Digital Art:Artists can create stunning compositions that blend fantasy and reality.
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Graphic Design: Designers can infuse magical elements into their work for unique branding and visual storytelling.
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Entertainment Industry: Filmmakers and game developers can utilize this model for concept art and visual effects that require a blend of the fantastical and the real.
Popular Models
