# F Lite (texture mode)

> F Lite is a 10B parameter diffusion model created by Fal and Freepik, trained exclusively on copyright-safe and SFW content. This is a high texture density variant of the model.


## Overview

- **Endpoint**: `https://fal.run/fal-ai/f-lite/texture`
- **Model ID**: `fal-ai/f-lite/texture`
- **Category**: text-to-image
- **Kind**: inference
**Description**: F Lite is a 10B parameter diffusion model created by Fal and Freepik, trained exclusively on copyright-safe and SFW content. The model was trained on Freepik's internal dataset comprising approximately 80 million copyright-safe images, making it the first publicly available model of this scale trained exclusively on legally compliant and SFW content.



## Pricing

- **Price**: $0.025 per megapixels

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`, _required_):
  The prompt to generate an image from.
  - Examples: "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground."

- **`negative_prompt`** (`string`, _optional_):
  Negative Prompt for generation. Default value: `""`
  - Default: `""`
  - Examples: "Blurry, out of focus, low resolution, bad anatomy, ugly, deformed, poorly drawn, extra limbs"

- **`image_size`** (`ImageSize | Enum`, _optional_):
  The size of the generated image. Default value: `landscape_4_3`
  - Default: `"landscape_4_3"`
  - One of: ImageSize | Enum

- **`num_inference_steps`** (`integer`, _optional_):
  The number of inference steps to perform. Default value: `28`
  - Default: `28`
  - Range: `1` to `50`

- **`seed`** (`integer`, _optional_):
  The same seed and the same prompt given to the same version of the model
  will output the same image every time.

- **`guidance_scale`** (`float`, _optional_):
  The CFG (Classifier Free Guidance) scale is a measure of how close you want
  the model to stick to your prompt when looking for a related image to show you. Default value: `3.5`
  - Default: `3.5`
  - Range: `1` to `20`

- **`sync_mode`** (`boolean`, _optional_):
  If `True`, the media will be returned as a data URI and the output data won't be available in the request history.
  - Default: `false`

- **`num_images`** (`integer`, _optional_):
  The number of images to generate. Default value: `1`
  - Default: `1`
  - Range: `1` to `4`

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled. Default value: `true`
  - Default: `true`



**Required Parameters Example**:

```json
{
  "prompt": "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground."
}
```

**Full Example**:

```json
{
  "prompt": "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground.",
  "negative_prompt": "Blurry, out of focus, low resolution, bad anatomy, ugly, deformed, poorly drawn, extra limbs",
  "image_size": "landscape_4_3",
  "num_inference_steps": 28,
  "guidance_scale": 3.5,
  "num_images": 1,
  "enable_safety_checker": true
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<Image>`, _required_):
  The generated image files info.
  - Array of Image

- **`timings`** (`Timings`, _required_)

- **`seed`** (`integer`, _required_):
  Seed of the generated Image. It will be the same value of the one passed in the
  input or the randomly generated that was used in case none was passed.

- **`has_nsfw_concepts`** (`list<boolean>`, _required_):
  Whether the generated images contain NSFW concepts.
  - Array of boolean

- **`prompt`** (`string`, _required_):
  The prompt used for generating the image.



**Example Response**:

```json
{
  "images": [
    {
      "url": "",
      "content_type": "image/jpeg"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/f-lite/texture \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground."
   }'
```

### 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(
    "fal-ai/f-lite/texture",
    arguments={
        "prompt": "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground."
    },
    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("fal-ai/f-lite/texture", {
  input: {
    prompt: "Mount Fuji at sunset, with the iconic snow-capped peak silhouetted against a vibrant orange and purple sky. A tranquil lake in the foreground perfectly reflects the mountain and colorful sky. A few traditional Japanese cherry blossom trees frame the scene, with their delicate pink petals visible in the foreground."
  },
  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/fal-ai/f-lite/texture)
- [API Documentation](https://fal.ai/models/fal-ai/f-lite/texture/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/f-lite/texture)

### 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)