Create Room Interior Endpoint

Overview

V5 APIs Create Room Interior endpoint generates room interiror by modifing a picture of the room.

Together with the room image you can add your description of the desired result in a text prompt.

Request

--request POST 'https://modelslab.com/api/v5/interior' \

Make a POST request to https://modelslab.com/api/v5/interior endpoint and pass the required parameters in the request body.

Body Attributes

Parameter	Description	Values
key	Your API Key used for request authorization.	string
prompt	A text description of the things you want in the generated image.	string
init_image	Link to the initial image to be used as a reference.	URL
num_inference_steps	Number of denoising steps.	21, 31, 41
base64	Indicate if the image is a base64 encoded image.	"yes"/"no"
guidance_scale	Scale for classifier-free guidance.	Minimum: 1, Maximum: 20

Example

Body

Body

{
  "key":"",
  "init_image" : "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png",
  "prompt" : "room",
  "num_inference_steps" : 21,
  "base64" : "no",
  "guidance_scale" : 7
}

Request

JS

var myHeaders = new Headers();
myHeaders.append("Content-Type", "application/json");

var raw = JSON.stringify({
  "key": "",
  "init_image" : "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png",
  "prompt" : "room",
  "num_inference_steps" : 21,
  "base64" : "no",
  "guidance_scale" : 7
});

var requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch("https://modelslab.com/api/v5/interior", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

PHP

<?php

$payload = [
  "key" => "", 
  "init_image" => "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png", 
  "prompt" => "room", 
  "num_inference_steps" => 21, 
  "base64" : "no",
  "guidance_scale" => 7
];

$curl = curl_init();

curl_setopt_array($curl, array(
  CURLOPT_URL => 'https://modelslab.com/api/v5/interior',
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => '',
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 0,
  CURLOPT_FOLLOWLOCATION => true,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => 'POST',
  CURLOPT_POSTFIELDS => json_encode($payload),
  CURLOPT_HTTPHEADER => array(
    'Content-Type: application/json'
  ),
));

$response = curl_exec($curl);

curl_close($curl);
echo $response;

NODE

var request = require('request');
var options = {
  'method': 'POST',
  'url': 'https://modelslab.com/api/v5/interior',
  'headers': {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    "key": "",
    "init_image" : "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png",
    "prompt" : "room",
    "num_inference_steps" : 21,
    "base64" : "no",
    "guidance_scale" : 7
  })
};

request(options, function (error, response) {
  if (error) throw new Error(error);
  console.log(response.body);
});

PYTHON

import requests
import json

url = "https://modelslab.com/api/v5/interior"

payload = json.dumps({
  "key": "",
  "init_image" : "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png",
  "prompt" : "room",
  "num_inference_steps" : 21,
  "base64" : "no",
  "guidance_scale" : 7
})

headers = {
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

JAVA

OkHttpClient client = new OkHttpClient().newBuilder()
  .build();
MediaType mediaType = MediaType.parse("application/json");
RequestBody body = RequestBody.create(mediaType, "{\n  \"key\": \"\",\n  \"init_image\" : \"https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png\",\n    \"prompt\" : \"room\",\n  \"num_inference_steps\" : 21,\n  \"guidance_scale\" : 7\n}");
Request request = new Request.Builder()
  .url("https://modelslab.com/api/v5/interior")
  .method("POST", body)
  .addHeader("Content-Type", "application/json")
  .build();
Response response = client.newCall(request).execute();

Response

Example Response

{
    "status": "success",
    "generationTime": 8.02,
    "id": 99044687,
    "output": [
        "https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/0-a29592cc-2c98-46e1-ae7b-bb1d2550a578.png"
    ],
    "meta": {
        "prompt": " room",
        "model_id": "realistic-vision-v13",
        "controlnet_model": "mlsd",
        "controlnet_type": "mlsd",
        "negative_prompt": "",
        "scheduler": "EulerAncestralDiscreteScheduler",
        "safety_checker": "no",
        "auto_hint": "yes",
        "guess_mode": "no",
        "strength": 1,
        "W": 560,
        "H": 752,
        "guidance_scale": 7,
        "controlnet_conditioning_scale": 1,
        "seed": 1972171263,
        "multi_lingual": "no",
        "use_karras_sigmas": "no",
        "algorithm_type": "no",
        "safety_checker_type": "sensitive_content_text",
        "instant_response": "no",
        "tomesd": "no",
        "init_image": "https://huggingface.co/lllyasviel/sd-controlnet-mlsd/resolve/main/images/room.png",
        "mask_image": null,
        "control_image": null,
        "vae": null,
        "steps": 21,
        "full_url": "no",
        "upscale": "no",
        "n_samples": 1,
        "embeddings": null,
        "ip_adapter_id": null,
        "ip_adapter_scale": 0.6,
        "ip_adapter_image": null,
        "enhance_prompt": "no",
        "enhance_style": null,
        "lora": null,
        "lora_strength": 1,
        "temp": "no",
        "base64": "no",
        "clip_skip": 1,
        "file_prefix": "a29592cc-2c98-46e1-ae7b-bb1d2550a578.png"
    },
    "tip": "Get 20x faster image generation using enterprise plan. Click here : https://modelslab.com/enterprise"
}