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>",
  "prompt": "<string>",
  "model_id": "<string>",
  "negative_prompt": "<string>",
  "enhance_prompt": "yes",
  "width": 512,
  "height": 512,
  "samples": 1,
  "num_inference_steps": 31,
  "safety_checker": false,
  "safety_checker_type": "sensitive_content_text",
  "seed": 123,
  "guidance_scale": 7.5,
  "use_karras_sigmas": true,
  "algorithm_type": "none",
  "vae": "<string>",
  "lora_strength": 123,
  "lora_model": "<string>",
  "clip_skip": 2,
  "base64": false,
  "temp": false,
  "webhook": "<string>",
  "track_id": "<string>",
  "ip_adapter_scale": 0.5,
  "ip_adapter_image": "<string>",
  "multi_lingual": false,
  "upscale": false,
  "highres_fix": false
}
'

import requests

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

payload = {
    "key": "<string>",
    "prompt": "<string>",
    "model_id": "<string>",
    "negative_prompt": "<string>",
    "enhance_prompt": "yes",
    "width": 512,
    "height": 512,
    "samples": 1,
    "num_inference_steps": 31,
    "safety_checker": False,
    "safety_checker_type": "sensitive_content_text",
    "seed": 123,
    "guidance_scale": 7.5,
    "use_karras_sigmas": True,
    "algorithm_type": "none",
    "vae": "<string>",
    "lora_strength": 123,
    "lora_model": "<string>",
    "clip_skip": 2,
    "base64": False,
    "temp": False,
    "webhook": "<string>",
    "track_id": "<string>",
    "ip_adapter_scale": 0.5,
    "ip_adapter_image": "<string>",
    "multi_lingual": False,
    "upscale": False,
    "highres_fix": False
}
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>',
    prompt: '<string>',
    model_id: '<string>',
    negative_prompt: '<string>',
    enhance_prompt: 'yes',
    width: 512,
    height: 512,
    samples: 1,
    num_inference_steps: 31,
    safety_checker: false,
    safety_checker_type: 'sensitive_content_text',
    seed: 123,
    guidance_scale: 7.5,
    use_karras_sigmas: true,
    algorithm_type: 'none',
    vae: '<string>',
    lora_strength: 123,
    lora_model: '<string>',
    clip_skip: 2,
    base64: false,
    temp: false,
    webhook: '<string>',
    track_id: '<string>',
    ip_adapter_scale: 0.5,
    ip_adapter_image: '<string>',
    multi_lingual: false,
    upscale: false,
    highres_fix: false
  })
};

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>',
    'prompt' => '<string>',
    'model_id' => '<string>',
    'negative_prompt' => '<string>',
    'enhance_prompt' => 'yes',
    'width' => 512,
    'height' => 512,
    'samples' => 1,
    'num_inference_steps' => 31,
    'safety_checker' => false,
    'safety_checker_type' => 'sensitive_content_text',
    'seed' => 123,
    'guidance_scale' => 7.5,
    'use_karras_sigmas' => true,
    'algorithm_type' => 'none',
    'vae' => '<string>',
    'lora_strength' => 123,
    'lora_model' => '<string>',
    'clip_skip' => 2,
    'base64' => false,
    'temp' => false,
    'webhook' => '<string>',
    'track_id' => '<string>',
    'ip_adapter_scale' => 0.5,
    'ip_adapter_image' => '<string>',
    'multi_lingual' => false,
    'upscale' => false,
    'highres_fix' => false
  ]),
  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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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>",
  "prompt": "<string>",
  "model_id": "<string>",
  "negative_prompt": "<string>",
  "enhance_prompt": "yes",
  "width": 512,
  "height": 512,
  "samples": 1,
  "num_inference_steps": 31,
  "safety_checker": false,
  "safety_checker_type": "sensitive_content_text",
  "seed": 123,
  "guidance_scale": 7.5,
  "use_karras_sigmas": true,
  "algorithm_type": "none",
  "vae": "<string>",
  "lora_strength": 123,
  "lora_model": "<string>",
  "clip_skip": 2,
  "base64": false,
  "temp": false,
  "webhook": "<string>",
  "track_id": "<string>",
  "ip_adapter_scale": 0.5,
  "ip_adapter_image": "<string>",
  "multi_lingual": false,
  "upscale": false,
  "highres_fix": false
}
'

import requests

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

payload = {
    "key": "<string>",
    "prompt": "<string>",
    "model_id": "<string>",
    "negative_prompt": "<string>",
    "enhance_prompt": "yes",
    "width": 512,
    "height": 512,
    "samples": 1,
    "num_inference_steps": 31,
    "safety_checker": False,
    "safety_checker_type": "sensitive_content_text",
    "seed": 123,
    "guidance_scale": 7.5,
    "use_karras_sigmas": True,
    "algorithm_type": "none",
    "vae": "<string>",
    "lora_strength": 123,
    "lora_model": "<string>",
    "clip_skip": 2,
    "base64": False,
    "temp": False,
    "webhook": "<string>",
    "track_id": "<string>",
    "ip_adapter_scale": 0.5,
    "ip_adapter_image": "<string>",
    "multi_lingual": False,
    "upscale": False,
    "highres_fix": False
}
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>',
    prompt: '<string>',
    model_id: '<string>',
    negative_prompt: '<string>',
    enhance_prompt: 'yes',
    width: 512,
    height: 512,
    samples: 1,
    num_inference_steps: 31,
    safety_checker: false,
    safety_checker_type: 'sensitive_content_text',
    seed: 123,
    guidance_scale: 7.5,
    use_karras_sigmas: true,
    algorithm_type: 'none',
    vae: '<string>',
    lora_strength: 123,
    lora_model: '<string>',
    clip_skip: 2,
    base64: false,
    temp: false,
    webhook: '<string>',
    track_id: '<string>',
    ip_adapter_scale: 0.5,
    ip_adapter_image: '<string>',
    multi_lingual: false,
    upscale: false,
    highres_fix: false
  })
};

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>',
    'prompt' => '<string>',
    'model_id' => '<string>',
    'negative_prompt' => '<string>',
    'enhance_prompt' => 'yes',
    'width' => 512,
    'height' => 512,
    'samples' => 1,
    'num_inference_steps' => 31,
    'safety_checker' => false,
    'safety_checker_type' => 'sensitive_content_text',
    'seed' => 123,
    'guidance_scale' => 7.5,
    'use_karras_sigmas' => true,
    'algorithm_type' => 'none',
    'vae' => '<string>',
    'lora_strength' => 123,
    'lora_model' => '<string>',
    'clip_skip' => 2,
    'base64' => false,
    'temp' => false,
    'webhook' => '<string>',
    'track_id' => '<string>',
    'ip_adapter_scale' => 0.5,
    'ip_adapter_image' => '<string>',
    'multi_lingual' => false,
    'upscale' => false,
    'highres_fix' => false
  ]),
  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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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  \"prompt\": \"<string>\",\n  \"model_id\": \"<string>\",\n  \"negative_prompt\": \"<string>\",\n  \"enhance_prompt\": \"yes\",\n  \"width\": 512,\n  \"height\": 512,\n  \"samples\": 1,\n  \"num_inference_steps\": 31,\n  \"safety_checker\": false,\n  \"safety_checker_type\": \"sensitive_content_text\",\n  \"seed\": 123,\n  \"guidance_scale\": 7.5,\n  \"use_karras_sigmas\": true,\n  \"algorithm_type\": \"none\",\n  \"vae\": \"<string>\",\n  \"lora_strength\": 123,\n  \"lora_model\": \"<string>\",\n  \"clip_skip\": 2,\n  \"base64\": false,\n  \"temp\": false,\n  \"webhook\": \"<string>\",\n  \"track_id\": \"<string>\",\n  \"ip_adapter_scale\": 0.5,\n  \"ip_adapter_image\": \"<string>\",\n  \"multi_lingual\": false,\n  \"upscale\": false,\n  \"highres_fix\": false\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 FLUX model. FLUX excels at creating highly detailed, realistic images with excellent prompt adherence.

You can apply LoRA models to your FLUX generations using the lora_model parameter. To use multiple LoRAs, pass comma-separated LoRA model IDs to lora_model (e.g. "more_details,animie") and matching comma-separated weights to lora_strength (e.g. "0.8,0.6").

Request

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

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

Parameters

key

string

required

Your API key for authentication. Get one from your dashboard.

model_id

string

required

The model to use for generation. Use flux for the FLUX model.

prompt

string

required

Text description of the image you want to generate. Be specific and detailed for best results.

negative_prompt

string

Things you don’t want in the image. Example: “blurry, low quality, distorted”

width

integer

default:"512"

Image width in pixels. Must be between 256-1024 and divisible by 8.

height

integer

default:"512"

Image height in pixels. Must be between 256-1024 and divisible by 8.

samples

integer

default:"1"

Number of images to generate. Range: 1-4.

num_inference_steps

integer

default:"30"

Number of denoising steps. Higher values produce more detailed images but take longer. Range: 20-50.

guidance_scale

float

default:"7.5"

How closely to follow the prompt. Higher values = more literal interpretation. Range: 1-20.

safety_checker

string

default:"yes"

Enable NSFW content filter. Values: “yes” or “no”.

seed

integer

Random seed for reproducible results. Use the same seed with same parameters to get identical images.

clip_skip

integer

default:"2"

Number of CLIP layers to skip. Affects style interpretation. Range: 1-4.

lora_model

string

The LoRA model ID to apply during generation. For multiple LoRAs, pass comma-separated model IDs, e.g. "more_details,animie".

lora_strength

string

The strength (weight) applied to the LoRA model. For multiple LoRAs, pass comma-separated values matching the order of lora_model, e.g. "0.8,0.6".

webhook

string

URL to receive a POST request when generation completes. See Webhooks.

track_id

string

Your custom identifier to track this request. Returned in response and webhook.

Request Example

{
    "key": "your_api_key",
    "model_id": "flux",
    "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": 512,
    "height": 512,
    "samples": 1,
    "num_inference_steps": 31,
    "safety_checker": "no",
    "seed": null,
    "guidance_scale": 7.5,
    "clip_skip": 2,
    "lora_model": "more_details",
    "lora_strength": "0.8",
    "webhook": null,
    "track_id": null
}

import requests

response = requests.post(
    "https://modelslab.com/api/v6/images/text2img",
    json={
        "key": "your_api_key",
        "model_id": "flux",
        "prompt": "ultra realistic portrait of a cyberpunk woman, blue eyes, cinematic lighting, 8K",
        "negative_prompt": "blurry, low quality, distorted",
        "width": 512,
        "height": 512,
        "samples": 1,
        "num_inference_steps": 31,
        "guidance_scale": 7.5
    }
)

data = response.json()
print(data)

const response = await fetch("https://modelslab.com/api/v6/images/text2img", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    key: "your_api_key",
    model_id: "flux",
    prompt: "ultra realistic portrait of a cyberpunk woman, blue eyes, cinematic lighting, 8K",
    negative_prompt: "blurry, low quality, distorted",
    width: 512,
    height: 512,
    samples: 1,
    num_inference_steps: 31,
    guidance_scale: 7.5
  })
});

const data = await response.json();
console.log(data);

curl -X POST "https://modelslab.com/api/v6/images/text2img" \
  -H "Content-Type: application/json" \
  -d '{
    "key": "your_api_key",
    "model_id": "flux",
    "prompt": "ultra realistic portrait of a cyberpunk woman, blue eyes, cinematic lighting, 8K",
    "negative_prompt": "blurry, low quality, distorted",
    "width": 512,
    "height": 512,
    "samples": 1,
    "num_inference_steps": 31,
    "guidance_scale": 7.5
  }'

Response

Success Response

When the image is generated successfully:

{
  "status": "success",
  "generationTime": 2.45,
  "id": 12345678,
  "output": [
    "https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/abc123.png"
  ],
  "proxy_links": {
    "0": "https://cdn.modelslab.com/generations/abc123.png"
  },
  "meta": {
    "prompt": "ultra realistic portrait...",
    "model_id": "flux",
    "width": 512,
    "height": 512,
    "seed": 12345
  }
}

Processing Response

For complex requests, you may receive a processing status:

{
  "status": "processing",
  "id": 12345678,
  "eta": 10,
  "message": "Your request is being processed"
}

When you receive a processing response, use the Fetch endpoint to poll for results.

Error Response

{
  "status": "error",
  "message": "prompt is required"
}

Response Fields

Field	Type	Description
`status`	string	”success”, “processing”, or “error”
`id`	integer	Unique request identifier
`output`	array	URLs of generated images
`proxy_links`	object	CDN-optimized URLs for faster delivery
`generationTime`	float	Time taken to generate (seconds)
`meta`	object	Generation parameters used

Tips for Better Results

Write Detailed Prompts

FLUX responds well to detailed, specific prompts. Include:

Subject description
Style (photorealistic, cinematic, etc.)
Lighting details
Camera settings (for photorealistic images)

Use Negative Prompts

Always include a negative prompt to avoid common issues:

"blurry, low quality, distorted, deformed, ugly, bad anatomy"

Adjust Guidance Scale

Lower (3-7): More creative, varied results
Higher (8-15): More literal prompt adherence
Default (7.5): Good balance for most use cases

Apply LoRA Models

Use the lora_model parameter to apply a LoRA and fine-tune the style or subject of your generation:

Pass a single LoRA ID, e.g. "more_details".
For multiple LoRAs, pass comma-separated IDs to lora_model and matching weights to lora_strength.
Tune lora_strength (typically 0.6–1.0) to control how strongly each LoRA is applied.

Image to Image

Transform existing images with FLUX

Fetch Results

Retrieve results for processing requests

Body

application/json

key

string

required

Your API Key used for request authorization.

prompt

string

required

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

model_id

string

required

The ID of the model to be used. It can be a public model or one you have trained.

negative_prompt

string

Items you don't want in the image.

enhance_prompt

boolean

default:yes

If true, the prompt will be enhanced for better results. Default is (true).

width

integer

default:512

The width of the image in pixels.

Required range: x <= 1024

height

integer

default:512

The height of the image in pixels.

Required range: x <= 1024

samples

integer

default:1

The number of images to be returned in response. Maximum is 4.

Required range: x <= 4

num_inference_steps

integer

default:31

The number of denoising steps. Values range from 1 to 20, and any value above 20 will be capped at 20.

Required range: 1 <= x <= 20

safety_checker

boolean

default:false

A checker for NSFW images. If detected, such images will be replaced by a blank image.

safety_checker_type

enum<string>

default:sensitive_content_text

How to modify the image if NSFW content is found.

Available options:

blur,

sensitive_content_text,

pixelate,

black

seed

integer | null

Seed for reproducible results. The same seed generates the same image. Pass null for a random number.

guidance_scale

number

default:7.5

Scale for classifier-free guidance.

Required range: 1 <= x <= 20

use_karras_sigmas

boolean

default:true

Use Karras sigmas to generate images. Produces nice results.

algorithm_type

enum<string>

default:none

Used in DPMSolverMultistepScheduler scheduler.

Available options:

none,

dpmsolver+++

vae

string | null

Use a custom VAE for generating images. Default is null.

lora_strength

integer

Strength of the LoRa model(s). If multiple LoRa models, provide comma-separated values (0.1 to 1).

lora_model

string

LoRa model ID(s). Multiple LoRa models are supported; pass comma-separated values (e.g., 'contrast-fix,yae-miko-genshin').

clip_skip

integer

default:2

Number of CLIP layers to skip.

Required range: 1 <= x <= 8

base64

boolean

default:false

If true, response output is base64 string. Input images can also be base64.

temp

boolean

default:false

If true, stores image in temporary storage (cleaned every 24 hours).

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.

ip_adapter_id

enum<string>

IP adapter ID.

Available options:

ip-adapter_sdxl,

ip-adapter_sd15,

ip-adapter-plus-face_sd15,

ip-adapter-plus_sdxl_vit-h,

ip-adapter-plus-face_sdxl_vit-h

ip_adapter_scale

number

Scale for the IP adapter (0 to 1).

Required range: 0 <= x <= 1

ip_adapter_image

string<uri>

Valid image URL for IP adapter.

scheduler

enum<string>

Available schedulers for image generation.

Available options:

DDPMScheduler,

DDIMScheduler,

PNDMScheduler,

LMSDiscreteScheduler,

EulerDiscreteScheduler,

EulerAncestralDiscreteScheduler,

DPMSolverMultistepScheduler,

HeunDiscreteScheduler,

KDPM2DiscreteScheduler,

DPMSolverSinglestepScheduler,

KDPM2AncestralDiscreteScheduler,

UniPCMultistepScheduler,

DDIMInverseScheduler,

DEISMultistepScheduler,

IPNDMScheduler,

KarrasVeScheduler,

ScoreSdeVeScheduler,

LCMScheduler

multi_lingual

boolean

default:false

Allow multilingual prompts. Set to 'true' if using a language other than English.

upscale

boolean

default:false

Set to 'true' to upscale the image resolution two times (2x).

highres_fix

boolean

default:false

Enable high-resolution fix for generated images.

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

Parameters

Request Example

Response

Success Response

Processing Response

Error Response

Response Fields

Tips for Better Results

Image to Image

Fetch Results

Body

Response

​Request

​Parameters

​Request Example

​Response

​Success Response

​Processing Response

​Error Response

​Response Fields

​Tips for Better Results

​Related Endpoints

Image to Image

Fetch Results

Body

Response

Request

Parameters

Request Example

Response

Success Response

Processing Response

Error Response

Response Fields

Tips for Better Results

Related Endpoints