SD 1.5

Embeddings are vector representations of words or phrases. In textual inversion, embeddings are used to capture the semantic meaning of the new concept or style that you want to teach the model. The model learns to associate the embedding with the example images, allowing it to generate images that are consistent with the concept or style.

To use embeddings, you will need to mention the ID of the embeddings model in model_id and you will need to include the trigger words from the embedding model in the prompt.

Tip

You can pass the words in the negative and/or the positive prompt based on how the model was trained.
Embeddings models can be used for text2image, image2image and inpainting tasks.

Text2Image
Image2Image
Inpainting

Python
JavaScript
PHP

import requests
import json
url = "https://api.imagepipeline.io/sd/text2image/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
    "model_id":  "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
    "prompt": "A handsome man in a white shirt in the woods, close-up, photorealisitic",
    "negative_prompt": "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject, deformed eyes,  UnrealisiticDream",
    "num_inference_steps": 20,
    "refiner": false,
    "samples": 1,
    "guidance_scale": 7.5,
    "width": 512,
    "height": 512,
    "embeddings": "f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
    "seed": 12345,
    "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

const url = "https://api.imagepipeline.io/sd/text2image/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
    "model_id":  "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
    "prompt": "A handsome man in a white shirt in the woods, close-up, photorealisitic",
    "negative_prompt": "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject, deformed eyes,  UnrealisiticDream",
    "num_inference_steps": 20,
    "refiner": false,
    "samples": 1,
    "guidance_scale": 7.5,
    "width": 512,
    "height": 512,
    "embeddings": "f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
    "seed": 12345,
    "safety_checker": true
};
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/sd/text2image/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
    "model_id"=> "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
    "prompt"=> "A handsome man in a white shirt in the woods, close-up, photorealisitic",
    "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, bad proportions, incomplete subject, deformed eyes,  UnrealisiticDream",
    "num_inference_steps"=> 40,
    "refiner"=> false,
    "samples"=> 1,
    "guidance_scale"=> 7.5,
    "width"=> 512,
    "height"=> 512,
    "embeddings"=> "f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
    "seed"=> 12345,
    "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);
?>

Python
JavaScript
PHP

import requests
import json
url = "https://api.imagepipeline.io/sd/image2image/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
  "model_id":  "sd1.5",
  "init_image": "https://docs.imagepipeline.io/img/cottage.png",
  "prompt": "gingerbread house with fairy lights and photorealistic finish, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, UnrealisticDream",
  "num_inference_steps": 37,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 8.5,
  "width": 512,
  "height": 512,
  "embeddings":"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed": 12345,
  "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

const url = "https://api.imagepipeline.io/sd/image2image/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
  "model_id":  "sd1.5",
  "init_image": "https://docs.imagepipeline.io/img/cottage.png",
  "prompt": "gingerbread house with fairy lights and photorealistic finish, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, UnrealisticDream",
  "num_inference_steps": 37,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 8.5,
  "width": 512,
  "height": 512,
  "embeddings":"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed": 12345,
  "safety_checker": true
};
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/sd/image2image/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
  "model_id"=>  "sd1.5",
  "init_image"=> "https://docs.imagepipeline.io/img/cottage.png",
  "prompt"=> "gingerbread house with fairy lights and photorealistic finish, 8k quality",
  "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, UnrealisticDream",
  "num_inference_steps"=> 37,
  "refiner"=> false,
  "samples"=> 1,
  "guidance_scale"=> 8.5,
  "width"=> 512,
  "height"=> 512,
  "embeddings"=>"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed" => 12345,
  "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);
?>

Python
JavaScript
PHP

import requests
import json
url = "https://api.imagepipeline.io/sd/inpainting/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
  "model_id":  "sd1.5",
  "init_image": "https://docs.imagepipeline.io/img/image_pipeline_img2img.png",
  "mask_image": "https://docs.imagepipeline.io/img/mask_image.png",
  "prompt": "lego batman next to the gingerbread house, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, UnrealisticDream",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "embeddings":"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed": 12345,
  "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

const url = "https://api.imagepipeline.io/sd/inpainting/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
  "model_id":  "sd1.5",
  "init_image": "https://docs.imagepipeline.io/img/image_pipeline_img2img.png",
  "mask_image": "https://docs.imagepipeline.io/img/mask_image.png",
  "prompt": "lego batman next to the gingerbread house, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, "UnrealisticDream",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "embeddings":"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed": 12345,
  "safety_checker": true
};
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/sd/inpainting/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
  "model_id"=>  "sd1.5",
  "init_image"=> "https://docs.imagepipeline.io/img/image_pipeline_img2img.png",
  "mask_image"=> "https://docs.imagepipeline.io/img/mask_image.png",
  "prompt"=> "lego batman next to the gingerbread house, photorealistic, 8k quality",
  "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, UnrealisticDream",
  "num_inference_steps"=> 40,
  "refiner"=> false,
  "samples"=> 1,
  "guidance_scale"=> 7.5,
  "width"=> 512,
  "height"=> 512,
  "embeddings"=>"f84133b4-aad8-44be-b9ce-7e7e3a8c111f",
  "seed"=> 12345,
  "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 SD-1.5 models
prompt	`str, 75 tokens`	Check our Prompt Guide for tips. Please add embeddings prompts to your prompt
negative_prompt	`str, 75 tokens`	Check our Prompt Guide for tips. Please add embeddings prompts to your prompt
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 512 for best results
height	`int`	Height in pixels. Lower than or equal to 512 for best results
seed	`int`	Controlling the seed can help you generate reproducible images
embeddings	`str, array`	Pass the model_id of embeddings models that can be found in models page
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.

Use the respective endpoint to fetch the status:

https://api.imagepipeline.io/sd/text2image/v1/status/{{id}}
https://api.imagepipeline.io/sd/image2image/v1/status/{{id}}
https://api.imagepipeline.io/sd/inpainting/v1/status/{{id}}

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