# flux-2-lora

{% columns %}
{% column width="66.66666666666666%" %}
{% hint style="info" %}
This documentation is valid for the following list of our models:

* `blackforestlabs/flux-2-lora`
  {% endhint %}
  {% endcolumn %}

{% column width="33.33333333333334%" %} <a href="https://aimlapi.com/app/blackforestlabs/flux-2-lora" class="button primary">Try in Playground</a>
{% endcolumn %}
{% endcolumns %}

## Model Overview

This text-to-image model enables you to apply your trained LoRA[^1] adapters, producing domain-specific outputs aligned with your brand aesthetic, expert content areas, or specialized visual constraints.

## Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our [Quickstart guide](https://docs.aimlapi.com/quickstart/setting-up).

## API Schema

## POST /v1/images/generations

>

```json
{"openapi":"3.0.0","info":{"title":"AIML API","version":"1.0.0"},"servers":[{"url":"https://api.aimlapi.com"}],"paths":{"/v1/images/generations":{"post":{"operationId":"_v1_images_generations","requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"model":{"type":"string","enum":["blackforestlabs/flux-2-lora"]},"prompt":{"type":"string","maxLength":4000,"description":"The text prompt describing the content, style, or composition of the image to be generated."},"image_size":{"anyOf":[{"type":"object","properties":{"width":{"type":"integer","minimum":512,"maximum":2048,"default":1024},"height":{"type":"integer","minimum":512,"maximum":2048,"default":768}},"description":"For both height and width, the value must be a multiple of 32."},{"type":"string","enum":["square_hd","square","portrait_4_3","portrait_16_9","landscape_4_3","landscape_16_9"],"description":"The size of the generated image."}],"default":"landscape_4_3"},"output_format":{"type":"string","enum":["jpeg","png","webp"],"default":"png","description":"The format of the generated image."},"enable_prompt_expansion":{"type":"boolean","default":true,"description":"If set to True, prompt will be upsampled with more details."},"num_images":{"type":"number","minimum":1,"maximum":4,"default":1,"description":"The number of images to generate."},"seed":{"type":"integer","minimum":1,"description":"The same seed and the same prompt given to the same version of the model will output the same image every time."},"guidance_scale":{"type":"number","minimum":0,"maximum":20,"description":"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."},"num_inference_steps":{"type":"integer","minimum":4,"maximum":50,"description":"The number of inference steps to perform."},"acceleration":{"type":"string","enum":["none","regular","high"],"default":"regular","description":"The speed of the generation. The higher the speed, the faster the generation."},"enable_safety_checker":{"type":"boolean","default":true,"description":"If set to True, the safety checker will be enabled."},"loras":{"type":"array","items":{"type":"object","properties":{"path":{"type":"string","description":"URL, HuggingFace repo ID (owner/repo)."},"scale":{"type":"number","minimum":0,"maximum":4,"description":"Scale factor for LoRA application."}},"required":["path"]},"maxItems":3,"description":"List of LoRA weights to apply (maximum 3). Each LoRA can be a URL, HuggingFace repo ID, or local path."}},"required":["model","prompt"],"title":"blackforestlabs/flux-2-lora"}}}},"responses":{"200":{"content":{"application/json":{"schema":{"type":"object","properties":{"data":{"type":"array","nullable":true,"items":{"type":"object","properties":{"url":{"type":"string","nullable":true,"description":"The URL where the file can be downloaded from."},"b64_json":{"type":"string","nullable":true,"description":"The base64-encoded JSON of the generated image."}}},"description":"The list of generated images."},"meta":{"type":"object","nullable":true,"properties":{"usage":{"type":"object","nullable":true,"properties":{"credits_used":{"type":"number","description":"The number of tokens consumed during generation."}},"required":["credits_used"]}},"description":"Additional details about the generation."}}}}}}}}}}}
```

## Quick Example

Let's generate an image of the specified size using a simple prompt.

{% tabs %}
{% tab title="Python" %}
{% code overflow="wrap" %}

```python
import requests
import json  # for getting a structured output with indentation

def main():
    response = requests.post(
        "https://api.aimlapi.com/v1/images/generations",
        headers={
            # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
            "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
            "Content-Type": "application/json"
        },
        json={
            "model": "blackforestlabs/flux-2-lora",
            "prompt": "A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses.",
            "image_size": {
                "width": 1472,
                "height": 512 
            }
        }
    )
    
    data = response.json()
    print(json.dumps(data, indent=2, ensure_ascii=False))

if __name__ == "__main__": 
    main()   
```

{% endcode %}
{% endtab %}

{% tab title="JS" %}
{% code overflow="wrap" %}

```javascript
async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/images/generations', {
    method: 'POST',
    headers: {
      // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'blackforestlabs/flux-2-lora',
      prompt: 'A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses.',
      image_size: {
        width: 1472,
        height: 512
      },
    }),
  });

  const data = await response.json();
  console.log('Generation:', data);
}

main();
```

{% endcode %}
{% endtab %}
{% endtabs %}

<details>

<summary>Response</summary>

{% code overflow="wrap" %}

```json
{
  "data": [
    {
      "url": "https://cdn.aimlapi.com/flamingo/files/b/0a847b04/UNSH9jzS_1AHujNGtda30.png"
    }
  ],
  "meta": {
    "usage": {
      "tokens_used": 44100
    }
  }
}
```

{% endcode %}

</details>

We obtained the following nice 1472x512 image by running this code example:

<div align="left"><figure><img src="https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-53e1180c5867d79b8dd96d864aac89921c47095a%2FUNSH9jzS_1AHujNGtda30.png?alt=media" alt=""><figcaption><p><code>"A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses."</code></p></figcaption></figure></div>

[^1]: The **LoRA algorithm** (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique used to adapt large language models (LLMs) and stable diffusion models to new tasks or domains without retraining the entire model. This process is faster and requires significantly less memory and computational resources than full fine-tuning.