# Fibo Lite

> Convert plain text into Fibo-Lite's transparent JSON-structured prompts - Bria's unique controllability layer that no closed model offers. Built for agentic and enterprise workflows.


## Overview

- **Endpoint**: `https://fal.run/bria/fibo-lite/generate/structured_prompt`
- **Model ID**: `bria/fibo-lite/generate/structured_prompt`
- **Category**: text-to-json
- **Kind**: inference
**Tags**: bria, fibo, structured-prompt



## Pricing

- **Price**: $0.008 per requests

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:


- **`prompt`** (`string`, _optional_):
  The prompt to generate.
  - Examples: "Oil painting of a fluffy, wide-eyed cat sitting upright, holding a small wooden sign reading \"Feed Me.\" Rich textures, dramatic brushstrokes, warm tones, and vintage charm."

- **`structured_prompt`** (`StructuredPrompt`, _optional_):
  The structured prompt to generate.

- **`image_url`** (`string`, _optional_):
  Input image URL

- **`seed`** (`integer`, _optional_):
  Seed for the random number generator. Default value: `7`
  - Default: `7`



**Required Parameters Example**:

```json
{}
```

**Full Example**:

```json
{
  "prompt": "Oil painting of a fluffy, wide-eyed cat sitting upright, holding a small wooden sign reading \"Feed Me.\" Rich textures, dramatic brushstrokes, warm tones, and vintage charm.",
  "seed": 7
}
```


### Output Schema

The API returns the following output format:

- **`short_description`** (`string`, _required_):
  A concise summary of the image content, 200 words maximum.

- **`objects`** (`list<ObjectDescription>`, _required_):
  List of prominent foreground/midground objects.
  - Array of ObjectDescription

- **`background_setting`** (`string`, _required_):
  Describe the overall environment, setting, or background, including any notable background elements.

- **`lighting`** (`LightingDetails`, _required_):
  Details about the lighting.

- **`aesthetics`** (`AestheticsDetails`, _required_):
  Details about the image aesthetics.

- **`photographic_characteristics`** (`PhotographicCharacteristicsDetails`, _optional_):
  Details about photographic characteristics.

- **`style_medium`** (`string`, _optional_):
  Identify the artistic style or medium.

- **`text_render`** (`list<TextRender>`, _optional_):
  List of text renders in the image.
  - Array of TextRender

- **`context`** (`string`, _required_):
  Provide any additional context that helps understand the image better.

- **`artistic_style`** (`string`, _required_):
  describe specific artistic characteristics, 3 words maximum.

- **`subject_emotions`** (`string`, _optional_):
  Explicitly describe any visible emotions or expressions on subjects.



**Example Response**:

```json
{
  "short_description": "",
  "objects": [
    {
      "description": "",
      "location": "",
      "relationship": ""
    }
  ],
  "background_setting": "",
  "lighting": {
    "conditions": "",
    "direction": ""
  },
  "aesthetics": {
    "composition": "",
    "color_scheme": "",
    "mood_atmosphere": "",
    "aesthetic_score": "",
    "preference_score": ""
  },
  "text_render": [
    {
      "text": "",
      "location": "",
      "size": "",
      "color": "",
      "font": ""
    }
  ],
  "context": "",
  "artistic_style": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/bria/fibo-lite/generate/structured_prompt \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{}'
```

### 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-lite/generate/structured_prompt",
    arguments={},
    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-lite/generate/structured_prompt", {
  input: {},
  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-lite/generate/structured_prompt)
- [API Documentation](https://fal.ai/models/bria/fibo-lite/generate/structured_prompt/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=bria/fibo-lite/generate/structured_prompt)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`