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  "prompt": "cinematic photo kung-fu-panda in mountains",
17  "negative_prompt": "low quality, ugly, painting",
18  "samples": 1,
19  "scheduler": "UniPC",
20  "num_inference_steps": 30,
21  "guidance_scale": 7.5,
22  "seed": 5357285110,
23  "controlnet_scale": 0.5,
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

promptstr *

Prompt to render

negative_promptstr ( default: None )

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

samplesint ( default: 1 ) Affects Pricing

Number of samples to generate.

min : 1,

min : 4

schedulerenum:str ( default: DPM2 Karras )

Type of scheduler.

Allowed values:

num_inference_stepsint ( default: 30 ) Affects Pricing

Number of denoising steps.

min : 20,

min : 100

guidance_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 1,

min : 25

seedint ( default: -1 )

Seed for image generation.

min : -1,

min : 999999999999999

controlnet_scalefloat ( default: 7.5 )

Scale for classifier-free guidance

min : 0,

min : 1

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.

Segmind Stable Diffusion 1B (SSD-1B) Depth Model

Segmind Stable Diffusion 1B (SSD-1B) Depth Model transcends traditional image processing by generating depth maps that convert flat visuals into rich, three-dimensional experiences. The resulting images are not just seen but felt, as they offer a tangible sense of depth that elevates the visual narrative.

At its core, the SSD-1B Depth Model utilizes advanced algorithms to interpret and render depth from two-dimensional images. It meticulously analyzes image masks to gauge depth variations, crafting a multi-layered depth map that breathes life into each pixel. While its depth perception is profound, the model's intelligence can sometimes extrapolate beyond the visible, introducing unexpected elements into the scene, particularly with images that defy natural structures.


  1. Realistic Depth Rendering: Elevates 2D images with a convincing sense of depth, making visuals more engaging and realistic.

  2. Dynamic Image Creation: Produces images that virtually leap from the screen, captivating the audience with their realism.

  3. Sophisticated Mask Analysis: Employs complex mask analysis to accurately render the depth of various elements within an image.

Use Cases

  1. 3D Visualizations: Transform architectural plans or product designs into interactive 3D models that offer a true sense of space and depth.

  2. Artistic Innovation: Artists can utilize the depth model to create visually stunning pieces that draw viewers into the scene.

  3. Enhanced Image Editing: Provide a new dimension to flat images, turning them into more realistic and engaging visuals.

  4. Game Environment Design: Implement in gaming to craft environments that offer a more authentic and immersive experience.