API
If you're looking for an API, you can choose from your desired programming language.
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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-fruitfusion"
# Request payload
data = {
"prompt": "few large strawberries falling into a pink liquid, milk bath photography, strawberry, slow - mo high speed photography, flowing milk, realistic jelly splashes, super high speed photography, berries dripping juice, fight with strawberries, strawberry granules, inspired by Alberto Seveso, berries dripping, high speed photography, award winning macro photography, culinary art photography, splash image",
"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",
"scheduler": "euler_a",
"num_inference_steps": 20,
"guidance_scale": 7.5,
"samples": 1,
"seed": 38330276112,
"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 imageAttributes
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.
Fruit Fusion
The Fruit Fusion model is built on Stable Diffusion 1.5, tailored for fruit imagery. Its training on a diverse range of fruit images ensures that the generated outputs are not only realistic but also capture the true essence and texture of the fruits, from the sheen of a fresh apple to the intricate patterns of a ripe melon.
Use cases
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Hyper-Realistic Outputs: Fruit Fusion's core strength lies in its ability to produce images that mirror the real-world appearance of fruits.
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Diverse Fruit Imagery: Trained on a wide array of fruit images, the model can generate visuals of virtually any fruit with impeccable detail.
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Optimized for Stock Images: The model's high-resolution and realistic outputs make it ideal for creating premium stock images.
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User-Centric Design: Tailored to meet the needs of photographers, marketers, and content creators, the model offers an intuitive platform for fruit image generation.
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Versatile Applications: Beyond stock images, the model can be used for educational purposes, digital art, and more.
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