# Bria GenFill V2

> Fill a masked region of your image with new content from a text instruction, with commercially safe outputs cleared for risk-free business use.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/bria/genfill/v2`
- **Model ID**: `bria/genfill/v2`
- **Category**: image-to-image
- **Kind**: inference
- **Tags**: bria, genfill, v2, inpaint, generative-fill, image-editing

## Pricing

- **Price**: $0.04 per megapixel

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/bria/genfill/v2` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/bria/genfill/v2/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/bria/genfill/v2/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/bria/genfill/v2/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`seed`** (`integer`, _optional_):
  Random seed for reproducibility. The same seed, instruction, image, and mask produce the same result.
  - Default: `5555`

- **`mask_url`** (`string`, _required_):
  A binary black-and-white mask the same size as the source image. White marks the region to fill; black is preserved.

- **`image_url`** (`string`, _required_):
  The source image to edit.

- **`steps_num`** (`integer`, _optional_):
  Number of inference steps (20-50). Higher values can improve detail at the cost of latency.
  - Default: `30`
  - Range: `20` to `50`

- **`instruction`** (`string`, _required_):
  Describe what should be generated inside the masked region of the source image.

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "seed": 5555,
  "mask_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower_input_mask.png",
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower.png",
  "steps_num": 20,
  "instruction": "A beautiful colorful butterfly"
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/gXG754fOrxjnA7AYnjXA8.png"
]
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/bria/genfill/v2 \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "seed": 5555,
  "mask_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower_input_mask.png",
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower.png",
  "steps_num": 20,
  "instruction": "A beautiful colorful butterfly"
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

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

const result = await modelrunner.subscribe("bria/genfill/v2", {
  input: {
  "seed": 5555,
  "mask_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower_input_mask.png",
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower.png",
  "steps_num": 20,
  "instruction": "A beautiful colorful butterfly"
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "bria/genfill/v2",
        arguments={
  "seed": 5555,
  "mask_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower_input_mask.png",
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Fibo_edit/flower.png",
  "steps_num": 20,
  "instruction": "A beautiful colorful butterfly"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/bria/genfill/v2)
- [OpenAPI Schema](https://modelrunner.ai/models/bria/genfill/v2/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/bria/genfill/v2/llms.txt)