SD 1.5

LoRA stands for Low-Rank Adaptation, a technique for fine-tuning text-to-image diffusion models like Stable Diffusion 1.5. It involves creating a small "LoRA" model that applies tiny changes to the standard checkpoint model. LoRA models are much smaller than checkpoint models, typically 2-200 MB in size compared to 2-7 GB for checkpoint models. This makes them more manageable and easier to load and generate images.

Tip

• Use a high guidance_scale value when using LoRAs
• You can use multiple LoRA models and specify corresponding weights that will determine how much each LoRA model will effect the end result

Text2Image

Python

import requests
import json
url = "https://api.imagepipeline.io/sd/text2image/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
  "model_id":  "sd1.5",
  "prompt": "Close up of beautiful young woman with red hair, standing in a field of wildflowers, wearing a flowing white dress, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "lora_models":["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights":["0.9"],
  "seed": 12345,
  "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

JavaScript

const url = "https://api.imagepipeline.io/sd/text2image/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
  "model_id":  "sd1.5",
  "prompt": "Close up of beautiful young woman with red hair, standing in a field of wildflowers, wearing a flowing white dress, photorealistic, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "lora_models":["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights":["0.9"],
  "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

<?php
$url = "https://api.imagepipeline.io/sd/text2image/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
  "model_id" =>  "sd1.5",
  "prompt" => "Close up of beautiful young woman with red hair, standing in a field of wildflowers, wearing a flowing white dress, photorealistic, 8k quality",
  "negative_prompt" => "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps": 20,
  "refiner" => false,
  "samples" => 1,
  "guidance_scale" => 7.5,
  "width" => 512,
  "height" => 512,
  "lora_models" => ["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights" => [0.9],
  "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);
?>

Image2Image

Python

import requests
import json
url = "https://api.imagepipeline.io/sd/image2image/v1"
headers = {
    "API-Key": "Your API Key"
}
data = {
  "model_id":  "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
  "prompt": "girl wearing flowers in hair, 8k quality",
  "init_image": "https://docs.imagepipeline.io/img/sdxl_text2image.png",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "lora_models":["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights": ["0.5"],
  "seed": 12345,
  "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

JavaScript

const url = "https://api.imagepipeline.io/sd/image2image/v1";
const headers = {
  "API-Key": "Your API Key",
  "Content-Type": "application/json"
};
const data = {
  "model_id":  "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
  "prompt": "girl wearing flowers in hair, 8k quality",
  "init_image": "https://docs.imagepipeline.io/img/sdxl_text2image.png",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps": 20,
  "refiner": false,
  "samples": 1,
  "guidance_scale": 7.5,
  "width": 512,
  "height": 512,
  "lora_models":["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights": ["0.5"],
  "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

<?php
$url = "https://api.imagepipeline.io/sd/image2image/v1";
$headers = array(
  "API-Key: Your-API-Key",
  "Content-Type: application/json"
);
$data = array(
  "model_id"=>  "38ff8ad2-d62a-457c-8b2c-182e48e564f2",
  "prompt"=> "girl wearing flowers in hair, 8k quality",
  "init_image"=> "https://docs.imagepipeline.io/img/sdxl_text2image.png",
  "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts, blurry face, ((deformed eyes)), ((deformed face)), bad proportion, (deformed iris), too many fingers",
  "num_inference_steps"=> 40,
  "refiner"=> false,
  "samples"=> 1,
  "guidance_scale"=> 7.5,
  "width"=> 512,
  "height"=> 512,
  "lora_models"=>["c7e734b3-db74-43c8-a50d-d1cc2fa150fc"],
  "lora_weights"=> ["0.5"],
  "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);
?>

Inpainting

Python

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": "astronaut legoai style, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts",
  "num_inference_steps": 45,
  "refiner": false,
  "samples": 2,
  "guidance_scale": 10.5,
  "width": 512,
  "height": 512,
  "lora_models": ["c9823e1c-3977-490c-be28-b9a87c81f751"],
  "lora_weights": ["2"],
  "seed": 12345,
  "safety_checker": true
}
response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json())

JavaScript

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": "astronaut legoai style, 8k quality",
  "negative_prompt": "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts",
  "num_inference_steps": 45,
  "refiner": false,
  "samples": 2,
  "guidance_scale": 10.5,
  "width": 512,
  "height": 512,
  "lora_models": ["c9823e1c-3977-490c-be28-b9a87c81f751"],
  "lora_weights": ["2"],
  "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

<?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"=> "astronaut legoai style, 8k quality",
  "negative_prompt"=> "error, cropped, worst quality, low quality, duplicate, blurry, (unfinished objects), stock image, artifacts",
  "num_inference_steps"=> 45,
  "refiner"=> false,
  "samples"=> 2,
  "guidance_scale"=> 10.5,
  "width"=> 512,
  "height"=> 512,
  "lora_models"=> ["c9823e1c-3977-490c-be28-b9a87c81f751"],
  "lora_weights"=> ["2"],
  "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
lora_models	str, array	Pass the model_id(s) of LoRA models that can be found in models page
lora_weights	str, array	Strength of the LoRA effect. Differs model to model
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.