# Fibo Edit [Blend]

> Complex, multi-step visual composition through natural language.


## Overview

- **Endpoint**: `https://fal.run/bria/fibo-edit/blend`
- **Model ID**: `bria/fibo-edit/blend`
- **Category**: image-to-image
- **Kind**: inference
**Tags**: bria, fibo-edit, blend, json



## Pricing

- **Price**: $0.04 per images

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`image_url`** (`string`, _required_):
  The source image.
  - Examples: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png"

- **`instruction`** (`string`, _required_):
  Instruct what elements you would like to blend in your image.
  - Examples: "Place the art on the shirt, keep the art exactly the same"



**Required Parameters Example**:

```json
{
  "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
  "instruction": "Place the art on the shirt, keep the art exactly the same"
}
```


### Output Schema

The API returns the following output format:

- **`image`** (`Image`, _required_):
  Generated image.

- **`images`** (`list<Image>`, _optional_):
  Generated images.
  - Default: `[]`
  - Array of Image

- **`structured_instruction`** (`Structured Instruction`, _required_):
  Current instruction.



**Example Response**:

```json
{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "images": []
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/bria/fibo-edit/blend \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
     "instruction": "Place the art on the shirt, keep the art exactly the same"
   }'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "bria/fibo-edit/blend",
    arguments={
        "image_url": "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
        "instruction": "Place the art on the shirt, keep the art exactly the same"
    },
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("bria/fibo-edit/blend", {
  input: {
    image_url: "https://bria-datasets.s3.us-east-1.amazonaws.com/Liza/shirt.png",
    instruction: "Place the art on the shirt, keep the art exactly the same"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/bria/fibo-edit/blend)
- [API Documentation](https://fal.ai/models/bria/fibo-edit/blend/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=bria/fibo-edit/blend)

### fal.ai Platform

- [Platform Documentation](https://docs.fal.ai)
- [Python Client](https://docs.fal.ai/clients/python)
- [JavaScript Client](https://docs.fal.ai/clients/javascript)