## Basic model info

- Model name: bria/bria genfill-v2
- Model description: Bria Genfill v2 generates new content from a prompt inside a masked region of an image, blending it with the surroundings; works best with blob-shaped masks. $0.04 per image.
- Endpoint name: image


## Model schema

The model schema is defined in the OpenAPI schema: [OpenAPI Schema](https://oapi.sunra.ai/main/bria/genfill-v2/latest.json)

### Model input schema

The model input schema is:



```json
{
  "description": "Input schema for Bria Genfill v2 (generate content inside a masked region).",
  "properties": {
    "seed": {
      "anyOf": [
        {
          "maximum": 2147483647,
          "minimum": 0,
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "description": "Random seed for reproducibility (the model uses 5555 when omitted).",
      "title": "Seed",
      "x-sr-order": 501
    },
    "image": {
      "anyOf": [
        {
          "format": "uri",
          "maxLength": 2083,
          "minLength": 1,
          "type": "string"
        },
        {
          "type": "string"
        }
      ],
      "description": "Source image (public URL).",
      "title": "Image",
      "x-sr-order": 301
    },
    "instruction": {
      "description": "What to generate inside the masked region (English), e.g. 'a small wooden sign'.",
      "maxLength": 2000,
      "minLength": 1,
      "title": "Instruction",
      "type": "string",
      "x-sr-order": 201
    },
    "mask": {
      "anyOf": [
        {
          "format": "uri",
          "maxLength": 2083,
          "minLength": 1,
          "type": "string"
        },
        {
          "type": "string"
        }
      ],
      "description": "Mask image the same size as the source: white where content should be generated, black elsewhere. Works best with blob-shaped masks.",
      "title": "Mask",
      "x-sr-order": 302
    },
    "steps": {
      "default": 30,
      "description": "Number of inference steps (20-50). Does not affect price.",
      "maximum": 50,
      "minimum": 20,
      "title": "Steps",
      "type": "integer",
      "x-sr-order": 401
    }
  },
  "required": [
    "instruction",
    "image",
    "mask"
  ],
  "title": "ImageInput",
  "type": "object"
}
```



### Model output schema

The model output schema is:



```json
{
  "properties": {
    "image": {
      "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"
    }
  },
  "required": [
    "image"
  ],
  "title": "ImageOutput",
  "type": "object"
}
```



## Example inputs and outputs

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

### Input example



```json
{
  "seed": null,
  "image": "",
  "instruction": "",
  "mask": "",
  "steps": 30
}
```



### Output example



```json
{
}
```



## Model code examples

### JavaScript



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

const result = await sunra.subscribe("bria/genfill-v2/image", {
  input: {
  	instruction: '',
  	image: '',
  	mask: '',
  	steps: 30,
  	seed: 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(
    "bria/genfill-v2/image",
    arguments={
        "instruction": "",
        "image": "",
        "mask": "",
        "steps": 30,
        "seed": 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(
    "bria/genfill-v2/image",
    SubscribeOptions.<JsonObject>builder()
        .input(Map.of(
            "instruction", "",
            "image", "",
            "mask", "",
            "steps", 30,
            "seed", 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 = "bria/genfill-v2/image",
    input = mapOf(
            "instruction" to "",
            "image" to "",
            "mask" to "",
            "steps" to 30,
            "seed" 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/bria/genfill-v2/image \
  --header "Authorization: Key $SUNRA_KEY" \
  --header "Content-Type: application/json" \
  --data '{"instruction":"","image":"","mask":"","steps":30,"seed":null}'
    
```



## Model readme

>