SDXL

The SDXL text-to-image diffusion model is capable of generating photo-realistic images from text descriptions. It is a deep learning model that was trained on a massive dataset of images and text captions. The model learns to associate the text with the corresponding images, and it can then use this knowledge to generate new images from text descriptions.

Python
JavaScript
php

import requests
import json
url = "https://api.imagepipeline.io/sdxl/text2image/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
  "model_id":  "d961a274-658c-4889-8c1a-bf85416cb1c1",
  "prompt": "woman wearing traditional Russian clothes, forest in the background, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject",
  "num_inference_steps": 30,
  "refiner": true,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 768,
  "height": 768,
  "safety_checker": true,
  "scheduler": "UniPCMultistepScheduler",
  seed: 12345
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

const url = "https://api.imagepipeline.io/sdxl/text2image/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
  "model_id":  "d961a274-658c-4889-8c1a-bf85416cb1c1",
  "prompt": "woman wearing traditional Russian clothes, forest in the background, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject",
  "num_inference_steps": 30,
  "refiner": true,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 768,
  "height": 768,
  "safety_checker": true,
  "scheduler": "UniPCMultistepScheduler",
  "seed": 12345
};
fetch(url, {
  method: "POST",
  headers: headers,
  body: JSON.stringify(data)
})
.then(response => response.json())
.then(data => console.log(data))
.catch((error) => console.error('Error:', error));

<?php
$url = "https://api.imagepipeline.io/sdxl/text2image/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
  "model_id"=>  "d961a274-658c-4889-8c1a-bf85416cb1c1",
  "prompt"=> "woman wearing traditional Russian clothes, forest in the background, photorealistic, 8k quality",
  "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject",
  "num_inference_steps"=> 30,
  "refiner"=> true,
  "samples"=> 1,
  "guidance_scale"=> 7.5,
  "width"=> 768,
  "height"=> 768,
  "seed" => 12345,
  "scheduler"=> "UniPCMultistepScheduler",
  "safety_checker" => true
);
$ch = curl_init($url);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
$response = curl_exec($ch);
if($response === FALSE){
  die(curl_error($ch));
}
$responseData = json_decode($response, TRUE);
curl_close($ch);
var_dump($responseData);
?>

JSON Parameters

Parameter	Permissible values	Notes
model_id	`str`	model_id can be found in models page. Filter by SDXL models
prompt	`str, 75 tokens`	Check our Prompt Guide for tips
negative_prompt	`str, 75 tokens`	Check our Prompt Guide for tips
num_inference_steps	`int, [1-100]`	Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 20-30
strength	`float, [0-1]`	Optional denoise strength
samples	`int, [1-4]`	Generates a maximum of 4 samples per API call
guidance_scale	`float, [1-20]`	Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5
width	`int`	Width in pixels. Lower than or equal to 768 for best results
height	`int`	Height in pixels. Lower than or equal to 768 for best results
seed	`int`	Controlling the seed can help you generate reproducible images
safety_checker	`boolean`	Checks for NSFW images and filters explicit images

Status

Your response will include a status.

If the status = SUCCESS, you will also have download_urls that will have the links to your generated image based on the number of samples you have entered. The maximum number of samples that can be generated is 4.
If the status = PENDING, you will receive a id. You can use the status endpoint to fetch your image using the id.
If the status = FAILURE, you will receive only an error message.

Endpoint:

https://api.imagepipeline.io/sdxl/text2image/v1/status/{{id}}

Pass the API-Key as the authorization in the header.