# FLUX 2 Lora

> Text-to-image generation with LoRA support for FLUX.2 [dev] from Black Forest Labs. Custom style adaptation and fine-tuned model variations.


## Overview

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


## Pricing

Your request will cost **$0.021** per megapixel. LoRAs over 2GB in size will accrue an extra 50% charge per GB (2-3GB total lora size will multiply the price by 1.5x, 3-4GB total lora size by 2x, 4-5GB total lora size by 2.5x. The max limit is now 5GB from the older 2GB.

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: "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."

- **`guidance_scale`** (`float`, _optional_):
  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: `2.5`
  - Default: `2.5`
  - Range: `0` to `20`

- **`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: `28`
  - Default: `28`
  - Range: `4` to `50`

- **`image_size`** (`ImageSize | Enum`, _optional_):
  The size of the image to generate. The width and height must be between 512 and 2048 pixels. 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`

- **`acceleration`** (`AccelerationEnum`, _optional_):
  The acceleration level to use for the image generation. Default value: `"regular"`
  - Default: `"regular"`
  - Options: `"none"`, `"regular"`, `"high"`
  - Examples: "regular"

- **`enable_prompt_expansion`** (`boolean`, _optional_):
  If set to true, the prompt will be expanded for better results.
  - Default: `false`

- **`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`

- **`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"`

- **`loras`** (`list<LoRAInput>`, _optional_):
  List of LoRA weights to apply (maximum 3). Each LoRA can be a URL, HuggingFace repo ID, or local path.
  - Default: `[]`
  - Array of LoRAInput



**Required Parameters Example**:

```json
{
  "prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."
}
```

**Full Example**:

```json
{
  "prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast.",
  "guidance_scale": 2.5,
  "num_inference_steps": 28,
  "image_size": "landscape_4_3",
  "num_images": 1,
  "acceleration": "regular",
  "enable_safety_checker": true,
  "output_format": "png",
  "loras": []
}
```


### Output Schema

The API returns the following output format:

- **`images`** (`list<ImageFile>`, _required_):
  The generated images
  - Array of ImageFile
  - Examples: [{"url":"https://storage.googleapis.com/falserverless/example_outputs/flux2_dev_lora_t2i_output.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://storage.googleapis.com/falserverless/example_outputs/flux2_dev_lora_t2i_output.png"
    }
  ],
  "prompt": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/fal-ai/flux-2/lora \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{
     "prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."
   }'
```

### 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/lora",
    arguments={
        "prompt": "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."
    },
    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/lora", {
  input: {
    prompt: "Close shot a pianist plays in a luxurious room with tall windows overlooking a rainy metropolis. Shot with a 50mm lens at a side profile angle, soft tungsten light highlighting hands moving over keys. Capture detailed reflections in polished black piano surfaces, raindrops sliding down glass, and atmospheric warm/cool lighting contrast."
  },
  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/lora)
- [API Documentation](https://fal.ai/models/fal-ai/flux-2/lora/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/flux-2/lora)

### 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)