1const axios = require('axios');
3const fs = require('fs');
4const path = require('path');
6async function toB64(imgPath) {
7    const data = fs.readFileSync(path.resolve(imgPath));
8    return Buffer.from(data).toString('base64');
11const api_key = "YOUR API-KEY";
12const url = "";
14const data = {
15  "image": "toB64('')",
16  "samples": 1,
17  "prompt": "royal chamber with fancy bed",
18  "negative_prompt": "Disfigured, cartoon, blurry, nude",
19  "scheduler": "UniPC",
20  "num_inference_steps": 25,
21  "guidance_scale": 7.5,
22  "strength": 1,
23  "seed": 131487365682,
24  "base64": false
27(async function() {
28    try {
29        const response = await, data, { headers: { 'x-api-key': api_key } });
30        console.log(;
31    } catch (error) {
32        console.error('Error:',;
33    }
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


imageimage *

Input Image

samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

min : 4

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

strengthfloat ( default: 1 )

How much to transform the reference image

min : 0.1,

min : 1

seedint ( default: -1 )

Seed for image generation.

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.

ControlNet SoftEdge

ControlNet SoftEdge model is designed to enhance diffusion models with added conditions. Going beyond traditional contours, ControlNet Softedge offers a refined approach, emphasizing the preservation of essential features while minimizing the prominence of brush strokes, leading to captivating visuals that resonate with depth and subtlety.

At the core of ControlNet Softedge lies an intricate neural network structure, meticulously crafted to condition diffusion models based on Soft edges. This unique approach ensures that while the fundamental features of an image are retained, the softer edges provide a seamless blend, eliminating the harshness often associated with rigid contours.


  1. Preservation of Features: Unlike traditional models, Softedge prioritizes the retention of core image features, ensuring authenticity.

  2. Superior Blending: Softedge's strength lies in its ability to merge and blend elements seamlessly, creating harmonious compositions.

  3. Flexibility over Rigid Contours: Offers a softer and more adaptable approach compared to sharp contours, providing artists with greater creative freedom.

Use Cases:

  1. Digital Artistry: Artists can leverage Softedge to create digital masterpieces that exude depth and subtlety.

  2. Image Enhancement: Ideal for refining images, eliminating harshness, and ensuring a seamless visual blend.

  3. Film and Animation: Animators can use Softedge to create scenes that require nuanced blending and merging of elements.

  4. Graphic Design: Designers can craft captivating visuals, from posters to digital ads, harnessing the model's blending prowess.

ControlNet SoftEdge License

ControlNet SoftEdge, in its commitment to ethical AI practices, has embraced the CreativeML OpenRAIL M license. This decision not only underscores the model's dedication to responsible AI but also aligns it with the principles set forth by BigScience and the RAIL Initiative. Their collaborative work in AI ethics and responsibility has set the benchmark for licenses like the OpenRAIL M.