# FLUX.1 Kontext [pro]

> Edit an existing image from a text instruction — change objects, style, background, or text — with high prompt adherence, legible typography, and commercial-usage rights.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/black-forest-labs/flux-kontext/pro`
- **Model ID**: `black-forest-labs/flux-kontext/pro`
- **Category**: image-to-image
- **Kind**: inference
- **Tags**: flux, flux-kontext, kontext, black-forest-labs, image-to-image, image-editing, instruction-edit, edit, pro

## Pricing

- **Price**: $0.04 per output

## 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/black-forest-labs/flux-kontext/pro` with a JSON body. The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/black-forest-labs/flux-kontext/pro/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/black-forest-labs/flux-kontext/pro/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/black-forest-labs/flux-kontext/pro/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 | null`, _optional_):
  Random seed. The same seed, prompt, and image produce the same result.

- **`prompt`** (`string`, _required_):
  The edit instruction. Describe the change to apply to the source image (e.g. 'put a donut next to the flour') and name anything that should stay the same.

- **`image_url`** (`string`, _required_):
  The source image to edit. Unmentioned regions are kept consistent while the described change is applied.

- **`num_images`** (`integer`, _optional_):
  The number of edited image variations to generate. Each generated image is billed separately.
  - Default: `1`
  - Range: `1` to `4`

- **`aspect_ratio`** (`AspectRatioEnum | null`, _optional_):
  Force the output aspect ratio. Leave unset to match the source image's aspect ratio.
  - Options: `"21:9"`, `"16:9"`, `"4:3"`, `"3:2"`, `"1:1"`, `"2:3"`, `"3:4"`, `"9:16"`, `"9:21"`

- **`output_format`** (`OutputFormatEnum`, _optional_):
  The format of the generated image.
  - Default: `"jpeg"`
  - Options: `"jpeg"`, `"png"`

- **`enhance_prompt`** (`boolean`, _optional_):
  Whether to automatically enhance the prompt for potentially better results.
  - Default: `false`

- **`guidance_scale`** (`number`, _optional_):
  How strongly the result follows the edit instruction. Raise for stronger adherence; lower if the edit looks over-applied.
  - Default: `3.5`
  - Range: `1` to `20`

- **`safety_tolerance`** (`SafetyToleranceEnum`, _optional_):
  Content-moderation strictness from '1' (strictest) to '6' (most permissive).
  - Default: `"2"`
  - Options: `"1"`, `"2"`, `"3"`, `"4"`, `"5"`, `"6"`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "Replace the plain sky with a vivid sunset filled with dramatic orange and purple clouds, leaving the buildings below unchanged.",
  "image_url": "https://media.modelrunner.ai/lIHWaA0znjyPvZStmHCtw.jpeg",
  "num_images": 1,
  "output_format": "jpeg",
  "enhance_prompt": false,
  "guidance_scale": 3.5,
  "safety_tolerance": "2"
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/a8tCCsju1ZfmJStxZcR44.jpeg"
]
```
## 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/black-forest-labs/flux-kontext/pro \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "Replace the plain sky with a vivid sunset filled with dramatic orange and purple clouds, leaving the buildings below unchanged.",
  "image_url": "https://media.modelrunner.ai/lIHWaA0znjyPvZStmHCtw.jpeg",
  "num_images": 1,
  "output_format": "jpeg",
  "enhance_prompt": false,
  "guidance_scale": 3.5,
  "safety_tolerance": "2"
}')

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("black-forest-labs/flux-kontext/pro", {
  input: {
  "prompt": "Replace the plain sky with a vivid sunset filled with dramatic orange and purple clouds, leaving the buildings below unchanged.",
  "image_url": "https://media.modelrunner.ai/lIHWaA0znjyPvZStmHCtw.jpeg",
  "num_images": 1,
  "output_format": "jpeg",
  "enhance_prompt": false,
  "guidance_scale": 3.5,
  "safety_tolerance": "2"
}
});

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

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "black-forest-labs/flux-kontext/pro",
        arguments={
  "prompt": "Replace the plain sky with a vivid sunset filled with dramatic orange and purple clouds, leaving the buildings below unchanged.",
  "image_url": "https://media.modelrunner.ai/lIHWaA0znjyPvZStmHCtw.jpeg",
  "num_images": 1,
  "output_format": "jpeg",
  "enhance_prompt": false,
  "guidance_scale": 3.5,
  "safety_tolerance": "2"
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/black-forest-labs/flux-kontext/pro)
- [OpenAPI Schema](https://modelrunner.ai/models/black-forest-labs/flux-kontext/pro/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/black-forest-labs/flux-kontext/pro/llms.txt)