1const axios = require('axios');
4const api_key = "YOUR API-KEY";
5const url = "";
7const data = {
8  "prompt": "a photo of Jason, solo, in frame, somber, apocalyptic city, dark theme, extremely detailed eyes, detailed symmetric realistic face extremely detailed natural skin texture, peach fuzz, messy hair, masterpiece, absurdres, artillery fire in the background, award winning ",
9  "negative_prompt": "airbrushed,3d, render, painting, anime, manga, illustration, (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,bige yes, teeth,nose piercing,(((extra arms)))cartoon,young,child ",
10  "scheduler": "dpmpp_sde_ancestral",
11  "num_inference_steps": 25,
12  "guidance_scale": 7.5,
13  "samples": 1,
14  "seed": 945216760,
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.

Paragon v1.0

The Paragon v1.0 model is a boon for artists seeking photorealism in their work. Developed by the collaborative efforts of s6yx, FIKOsofter, Lykon, and CornmeisterNL, this model is built on the Stable Diffusion 1.5 framework. It is renowned for its ability to generate super realistic portraits that are really close to real photographs. The model's strong contrast is a feature that has garnered much appreciation. It is versatile, capable of generating portraits in various styles, ages, and clothing. The LoRa versions of this model have been particularly well-received for their performance.

Delving into the technical architecture of Paragon v1.0, it is designed to work optimally with Euler A or DPM++ SDE Karras schedulers with 25 or more steps. The model's recommended CFG Scale ranges from 5 to 12, and it performs better with Clip Skip 2. The model also comes with a recommended negative prompt that includes terms like "deformed iris", "semi-realistic", "cgi", "3d", "render", "sketch", "cartoon", "drawing", "anime", "mutated hands and fingers", "deformed", "distorted", "disfigured", "poorly drawn", "bad anatomy", "wrong anatomy", "extra limb", "missing limb", "floating limbs", "disconnected limbs", "mutation", "mutated", "ugly", "disgusting", "amputation", and "easynegative".

The advantages of the Paragon v1.0 model are manifold. Its ability to create photorealistic portraits is unmatched, making it a valuable tool for artists who require high levels of realism in their work. The model's strong contrast adds depth and dimension to the portraits, enhancing their lifelike quality. Furthermore, the model's versatility in generating portraits of different styles, ages, and clothing expands the creative possibilities for artists.

Paragon v1.0 use cases:

  1. Digital Art Creation: Artists can use this model to create realistic portraits, adding a touch of realism to their digital art.
  2. Character Generation for Video Games or Animations: Game developers and animators can use this model to generate diverse characters, enhancing the visual appeal of their games or animations.
  3. Social Media or Virtual Reality Avatars: The model can be used to produce unique avatars for users on social media or virtual reality platforms.
  4. Character Design for Books or Graphic Novels: Authors and graphic novelists can use this model to design fictional characters, bringing their stories to life.
  5. Fashion Design Visualization: Fashion designers can use this model to visualize different styles and outfits on various models, aiding in the design process.

Paragon v1.0 license

The license for the Paragon v1.0 model, known as the "CreativeML Open RAIL-M" license, is designed to promote both open and responsible use of the model. You may add your own copyright statement to your modifications and provide additional or different license terms for your modifications. You are accountable for the output you generate using the model, and no use of the output can contravene any provision as stated in the license.