1const axios = require('axios');
4const api_key = "YOUR API-KEY";
5const url = "";
7const data = {
8  "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",
9  "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",
10  "scheduler": "euler_a",
11  "num_inference_steps": 20,
12  "guidance_scale": 7.5,
13  "samples": 1,
14  "seed": 38330276112,
15  "img_width": 512,
16  "img_height": 768,
17  "base64": false
20(async function() {
21    try {
22        const response = await, data, { headers: { 'x-api-key': api_key } });
23        console.log(;
24    } catch (error) {
25        console.error('Error:',;
26    }
HTTP Response Codes
200 - OKImage Generated
401 - UnauthorizedUser authentication failed
404 - Not FoundThe requested URL does not exist
405 - Method Not AllowedThe requested HTTP method is not allowed
406 - Not AcceptableNot enough credits
500 - Server ErrorServer had some issue with processing


promptstr *

Prompt to render

negative_promptstr ( default: None )

Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'

schedulerenum:str ( default: UniPC )

Type of scheduler.

Allowed values:

num_inference_stepsint ( default: 20 ) Affects Pricing

Number of denoising steps.

min : 20,

min : 100

guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0.1,

min : 25

samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

min : 4

seedint ( default: -1 )

Seed for image generation.

img_widthenum:int ( default: 512 ) Affects Pricing

Width of the image.

Allowed values:

img_heightenum:int ( default: 512 ) Affects Pricing

Height of the Image

Allowed values:

base64boolean ( default: 1 )

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

  1. Hyper-Realistic Outputs: Fruit Fusion's core strength lies in its ability to produce images that mirror the real-world appearance of fruits.

  2. Diverse Fruit Imagery: Trained on a wide array of fruit images, the model can generate visuals of virtually any fruit with impeccable detail.

  3. Optimized for Stock Images: The model's high-resolution and realistic outputs make it ideal for creating premium stock images.

  4. User-Centric Design: Tailored to meet the needs of photographers, marketers, and content creators, the model offers an intuitive platform for fruit image generation.

  5. Versatile Applications: Beyond stock images, the model can be used for educational purposes, digital art, and more.