## Basic model info

- Model name: recraft/recraft recraft-v4-pro
- Model description: Recraft V4 Pro was developed with designers to bring true visual taste to AI image generation. Built for brand systems and production-ready workflows, it goes beyond prompt accuracy — delivering stronger composition, refined lighting, realistic materials, and a cohesive aesthetic.
- Endpoint name: text-to-vector


## Model schema

The model schema is defined in the OpenAPI schema: [OpenAPI Schema](https://oapi.sunra.ai/main/recraft/recraft-v4-pro/latest.json)

### Model input schema

The model input schema is:



```json
{
  "properties": {
    "prompt": {
      "description": "The prompt for the vector image generation",
      "maxLength": 1000,
      "title": "Prompt",
      "type": "string",
      "x-sr-order": 201
    },
    "aspect_ratio": {
      "default": "1:1",
      "description": "Aspect ratio of the vector image",
      "enum": [
        "1:1",
        "4:3",
        "3:4",
        "16:9",
        "9:16"
      ],
      "title": "Aspect Ratio",
      "type": "string",
      "x-sr-order": 401
    },
    "background_color": {
      "anyOf": [
        {
          "description": "RGB color specification.",
          "properties": {
            "b": {
              "default": 0,
              "description": "Blue color value",
              "maximum": 255,
              "minimum": 0,
              "title": "B",
              "type": "integer"
            },
            "g": {
              "default": 0,
              "description": "Green color value",
              "maximum": 255,
              "minimum": 0,
              "title": "G",
              "type": "integer"
            },
            "r": {
              "default": 0,
              "description": "Red color value",
              "maximum": 255,
              "minimum": 0,
              "title": "R",
              "type": "integer"
            }
          },
          "title": "RGBColor",
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "description": "The preferable background color of the generated vector image",
      "x-sr-order": 403
    },
    "colors": {
      "description": "An array of preferable colors for the generated vector image",
      "items": {
        "description": "RGB color specification.",
        "properties": {
          "b": {
            "default": 0,
            "description": "Blue color value",
            "maximum": 255,
            "minimum": 0,
            "title": "B",
            "type": "integer"
          },
          "g": {
            "default": 0,
            "description": "Green color value",
            "maximum": 255,
            "minimum": 0,
            "title": "G",
            "type": "integer"
          },
          "r": {
            "default": 0,
            "description": "Red color value",
            "maximum": 255,
            "minimum": 0,
            "title": "R",
            "type": "integer"
          }
        },
        "title": "RGBColor",
        "type": "object"
      },
      "title": "Colors",
      "type": "array",
      "x-sr-order": 402
    }
  },
  "required": [
    "prompt"
  ],
  "title": "TextToVectorInput",
  "type": "object"
}
```



### Model output schema

The model output schema is:



```json
{
  "properties": {
    "images": {
      "items": {
        "properties": {
          "content_type": {
            "description": "The mime type of the file.",
            "title": "Content Type",
            "type": "string"
          },
          "file_name": {
            "description": "The name of the file. It will be auto-generated if not provided.",
            "title": "File Name",
            "type": "string"
          },
          "file_size": {
            "description": "The size of the file in bytes.",
            "title": "File Size",
            "type": "integer"
          },
          "url": {
            "description": "The URL where the file can be downloaded from.",
            "title": "Url",
            "type": "string"
          }
        },
        "required": [
          "content_type",
          "file_name",
          "file_size",
          "url"
        ],
        "title": "SunraFile",
        "type": "object"
      },
      "title": "Images",
      "type": "array"
    }
  },
  "required": [
    "images"
  ],
  "title": "ImagesOutput",
  "type": "object"
}
```



## Example inputs and outputs

Use the following example inputs and outputs to understand the model.

### Input example



```json
{
  "prompt": "",
  "aspect_ratio": "1:1",
  "background_color": null,
  "colors": [
  ]
}
```



### Output example



```json
{
}
```



## Model code examples

### JavaScript



```javascript
import { sunra } from "@sunra/client";

const result = await sunra.subscribe("recraft/recraft-v4-pro/text-to-vector", {
  input: {
  	prompt: '',
  	aspect_ratio: '1:1',
  	colors: [],
  	background_color: null
  },
  logs: true,
  onQueueUpdate: (update) => {
    console.log(`Status Update: ${update.status}, Request ID: ${update.request_id}`);
  },
});
console.log(result.data);
console.log(result.requestId);
```



### Python



```python
import sunra_client

result = sunra_client.subscribe(
    "recraft/recraft-v4-pro/text-to-vector",
    arguments={
        "prompt": "",
        "aspect_ratio": "1:1",
        "colors": [],
        "background_color": None
    },
    with_logs=True,
    on_enqueue=print,
    on_queue_update=print,
)
print(result)
```



### Java



```java
import ai.sunra.client.*;
import java.util.Map;
import com.google.gson.JsonObject;

var client = SunraClient.withEnvCredentials();

var response = client.subscribe(
    "recraft/recraft-v4-pro/text-to-vector",
    SubscribeOptions.<JsonObject>builder()
        .input(Map.of(
            "prompt", "",
            "aspect_ratio", "1:1",
            "colors", ,
            "background_color", null))
        .resultType(JsonObject.class)
        .onQueueUpdate(update -> System.out.printf(
            "\nStatus Update: %s, Request ID: %s%n",
            update.getStatus(),
            update.getRequestId()
        ))
        .logs(true)
        .build()
);

System.out.println("Completed!");
System.out.println(response.getData());
  
```



### Kotlin



```kotlin
import ai.sunra.client.kt.*
import com.google.gson.JsonObject

val client = createSunraClient()

val response = client.subscribe<JsonObject>(
    endpointId = "recraft/recraft-v4-pro/text-to-vector",
    input = mapOf(
            "prompt" to "",
            "aspect_ratio" to "1:1",
            "colors" to ,
            "background_color" to null),
    options = ai.sunra.client.kt.SubscribeOptions(logs = true),
    onUpdate = { update ->
        println("\nStatus Update: ${update.status}, Request ID: ${update.requestId}")
    }
)

println("Completed!")
println(response.data)
  
```



### Curl



```bash

curl --request POST \
  --url https://api.sunra.ai/v1/queue/recraft/recraft-v4-pro/text-to-vector \
  --header "Authorization: Key $SUNRA_KEY" \
  --header "Content-Type: application/json" \
  --data '{"prompt":"","aspect_ratio":"1:1","colors":[],"background_color":null}'
    
```



## Model readme

undefined