# FLUX.2 [klein] 4B

> Text-to-image generation with FLUX.2 [klein] 4B from Black Forest Labs. Enhanced realism, crisper text generation, and native editing capabilities.


## Overview

- **Endpoint**: `https://fal.run/fal-ai/flux-2/klein/4b`
- **Model ID**: `fal-ai/flux-2/klein/4b`
- **Category**: text-to-image
- **Kind**: inference


## Pricing

- **Price**: $0.005 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: "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography"

- **`seed`** (`integer`, _optional_):
  The seed to use for the generation. If not provided, a random seed will be used.

- **`num_inference_steps`** (`integer`, _optional_):
  The number of inference steps to perform. Default value: `4`
  - Default: `4`
  - Range: `4` to `8`

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

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

- **`sync_mode`** (`boolean`, _optional_):
  If `True`, the media will be returned as a data URI. Output is not stored when this is True.
  - Default: `false`

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

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



**Required Parameters Example**:

```json
{
  "prompt": "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography"
}
```

**Full Example**:

```json
{
  "prompt": "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography",
  "num_inference_steps": 4,
  "image_size": "landscape_4_3",
  "num_images": 1,
  "enable_safety_checker": true,
  "output_format": "png"
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<ImageFile>`, _required_):
  The generated images
  - Array of ImageFile
  - Examples: [{"url":"https://v3b.fal.media/files/b/0a8a69c7/gU9ZgfFC9oAZjpIoveAac.png"}]

- **`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": "https://v3b.fal.media/files/b/0a8a69c7/gU9ZgfFC9oAZjpIoveAac.png"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/flux-2/klein/4b \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography"
   }'
```

### 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/flux-2/klein/4b",
    arguments={
        "prompt": "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography"
    },
    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/flux-2/klein/4b", {
  input: {
    prompt: "Cheetah in pre-sprint crouch, muscles tensed, gazelle visible in distance, African savanna golden grass, dust particles in air, National Geographic wildlife photography"
  },
  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/flux-2/klein/4b)
- [API Documentation](https://fal.ai/models/fal-ai/flux-2/klein/4b/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/flux-2/klein/4b)

### 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)