Text to Image

Text-to-Image

curl --request POST \
  --url https://modelslab.com/api/v6/images/text2img \
  --header 'Content-Type: application/json' \
  --data '
{
  "key": "<string>",
  "model_id": "<string>",
  "prompt": "<string>",
  "negative_prompt": "<string>",
  "width": 1024,
  "height": 1024,
  "samples": 1,
  "num_inference_steps": 30,
  "guidance_scale": 7.5,
  "safety_checker": "yes",
  "seed": 123,
  "webhook": "<string>",
  "track_id": "<string>"
}
'

import requests

url = "https://modelslab.com/api/v6/images/text2img"

payload = {
    "key": "<string>",
    "model_id": "<string>",
    "prompt": "<string>",
    "negative_prompt": "<string>",
    "width": 1024,
    "height": 1024,
    "samples": 1,
    "num_inference_steps": 30,
    "guidance_scale": 7.5,
    "safety_checker": "yes",
    "seed": 123,
    "webhook": "<string>",
    "track_id": "<string>"
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    key: '<string>',
    model_id: '<string>',
    prompt: '<string>',
    negative_prompt: '<string>',
    width: 1024,
    height: 1024,
    samples: 1,
    num_inference_steps: 30,
    guidance_scale: 7.5,
    safety_checker: 'yes',
    seed: 123,
    webhook: '<string>',
    track_id: '<string>'
  })
};

fetch('https://modelslab.com/api/v6/images/text2img', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://modelslab.com/api/v6/images/text2img",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'key' => '<string>',
    'model_id' => '<string>',
    'prompt' => '<string>',
    'negative_prompt' => '<string>',
    'width' => 1024,
    'height' => 1024,
    'samples' => 1,
    'num_inference_steps' => 30,
    'guidance_scale' => 7.5,
    'safety_checker' => 'yes',
    'seed' => 123,
    'webhook' => '<string>',
    'track_id' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://modelslab.com/api/v6/images/text2img"

	payload := strings.NewReader("{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://modelslab.com/api/v6/images/text2img")
  .header("Content-Type", "application/json")
  .body("{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://modelslab.com/api/v6/images/text2img")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "status": "success",
  "generationTime": 123,
  "id": 123,
  "output": [
    "<string>"
  ],
  "proxy_links": [
    "<string>"
  ],
  "meta": {},
  "nsfw_content_detected": true,
  "webhook_status": "<string>",
  "tip": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

POST

text2img

Text-to-Image

curl --request POST \
  --url https://modelslab.com/api/v6/images/text2img \
  --header 'Content-Type: application/json' \
  --data '
{
  "key": "<string>",
  "model_id": "<string>",
  "prompt": "<string>",
  "negative_prompt": "<string>",
  "width": 1024,
  "height": 1024,
  "samples": 1,
  "num_inference_steps": 30,
  "guidance_scale": 7.5,
  "safety_checker": "yes",
  "seed": 123,
  "webhook": "<string>",
  "track_id": "<string>"
}
'

import requests

url = "https://modelslab.com/api/v6/images/text2img"

payload = {
    "key": "<string>",
    "model_id": "<string>",
    "prompt": "<string>",
    "negative_prompt": "<string>",
    "width": 1024,
    "height": 1024,
    "samples": 1,
    "num_inference_steps": 30,
    "guidance_scale": 7.5,
    "safety_checker": "yes",
    "seed": 123,
    "webhook": "<string>",
    "track_id": "<string>"
}
headers = {"Content-Type": "application/json"}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': 'application/json'},
  body: JSON.stringify({
    key: '<string>',
    model_id: '<string>',
    prompt: '<string>',
    negative_prompt: '<string>',
    width: 1024,
    height: 1024,
    samples: 1,
    num_inference_steps: 30,
    guidance_scale: 7.5,
    safety_checker: 'yes',
    seed: 123,
    webhook: '<string>',
    track_id: '<string>'
  })
};

fetch('https://modelslab.com/api/v6/images/text2img', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://modelslab.com/api/v6/images/text2img",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'key' => '<string>',
    'model_id' => '<string>',
    'prompt' => '<string>',
    'negative_prompt' => '<string>',
    'width' => 1024,
    'height' => 1024,
    'samples' => 1,
    'num_inference_steps' => 30,
    'guidance_scale' => 7.5,
    'safety_checker' => 'yes',
    'seed' => 123,
    'webhook' => '<string>',
    'track_id' => '<string>'
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://modelslab.com/api/v6/images/text2img"

	payload := strings.NewReader("{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://modelslab.com/api/v6/images/text2img")
  .header("Content-Type", "application/json")
  .body("{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://modelslab.com/api/v6/images/text2img")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n  \"key\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"prompt\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"width\": 1024,\n  \"height\": 1024,\n  \"samples\": 1,\n  \"num_inference_steps\": 30,\n  \"guidance_scale\": 7.5,\n  \"safety_checker\": \"yes\",\n  \"seed\": 123,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\"\n}"

response = http.request(request)
puts response.read_body

{
  "status": "success",
  "generationTime": 123,
  "id": 123,
  "output": [
    "<string>"
  ],
  "proxy_links": [
    "<string>"
  ],
  "meta": {},
  "nsfw_content_detected": true,
  "webhook_status": "<string>",
  "tip": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

{
  "status": "error",
  "message": "<string>"
}

Generate photorealistic images from text descriptions using the Krea 2 Turbo model. Krea 2 Turbo excels at creating highly detailed, realistic images with excellent prompt adherence.

Maximum dimensions are 1488×1200 or 1200×1488 (portrait/landscape). 1488×1488 is not supported.

Request

Make a POST request to the endpoint below with the required parameters.

POST https://modelslab.com/api/v6/images/text2img

Body

json

{
    "key": "your_api_key",
    "model_id": "krea-2-turbo",
    "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
    "negative_prompt": "blurry, low quality, distorted, deformed",
    "width": "1024",
    "height": "1024",
    "samples": "1",
    "num_inference_steps": "31",
    "safety_checker": "no",
    "seed": null,
    "guidance_scale": 7.5,
    "webhook": null,
    "track_id": null
}

Body

application/json

key

string

required

Your API Key used for request authorization.

model_id

string

required

The ID of the model to be used. Use krea-2-turbo for the Krea 2 Turbo model.

prompt

string

required

A text description of what you want in the generated image.

negative_prompt

string

Items you don't want in the image.

width

integer

default:1024

The width of the image in pixels. Must be between 1024-1488 and divisible by 8. When set to 1488, height must be 1200 (1488x1488 is not supported).

Required range: 1024 <= x <= 1488

height

integer

default:1024

The height of the image in pixels. Must be between 1024-1488 and divisible by 8. When set to 1488, width must be 1200 (1488x1488 is not supported).

Required range: 1024 <= x <= 1488

samples

integer

default:1

The number of images to be returned in response. Only 1 sample is supported.

Required range: x <= 1

num_inference_steps

integer

default:30

Number of denoising steps. Higher values produce more detailed images but take longer.

Required range: 20 <= x <= 50

guidance_scale

number

default:7.5

How closely to follow the prompt. Higher values = more literal interpretation.

Required range: 1 <= x <= 20

safety_checker

enum<string>

default:yes

Enable NSFW content filter.

Available options:

yes,

no

seed

integer | null

Random seed for reproducible results. Pass null for a random number.

webhook

string<uri>

URL to receive a POST API call once image generation is complete.

track_id

string

Unique ID used in webhook response to identify the request.

Response

Text-to-image generation response

Option 1
Option 2
Option 3

status

enum<string>

Status of the image generation.

Available options:

success

generationTime

number

Time taken to generate the image in seconds.

integer

Unique identifier for the image generation request.

output

string<uri>[]

Array of generated image URLs.

proxy_links

string<uri>[]

Array of proxy image URLs.

Using the APIs

Our AI APIs

Request

Body

Body

Response

​Request

​Body

Body

Response

Request

Body