# flux-2-lora-edit

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

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

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

## Model Overview

This image-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-edit"]},"prompt":{"type":"string","maxLength":4000,"description":"The text prompt describing the content, style, or composition of the image to be generated."},"image_urls":{"type":"array","items":{"type":"string","format":"uri"},"minItems":1,"maxItems":3,"description":"List of URLs or local Base64 encoded images to edit."},"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."},"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."},"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."},"num_images":{"type":"number","minimum":1,"maximum":4,"default":1,"description":"The number of images to generate."},"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","image_urls"],"title":"blackforestlabs/flux-2-lora-edit"}}}},"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 two input images and a prompt that defines how they should be edited.

{% 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-edit",
            "prompt": "Combine the images so the T-Rex is wearing a business suit, sitting in a cozy small café, drinking from the mug. Blur the background slightly to create a bokeh effect.",
            "image_urls": [
                "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/t-rex.png",
                "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/blue-mug.jpg"
            ],
            "guidance_scale": 19
        }
    )

    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-edit',
      prompt: 'Combine the images so the T-Rex is wearing a business suit, sitting in a cozy small café, drinking from the mug. Blur the background slightly to create a bokeh effect.',
      image_urls: [
                "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/t-rex.png",
                "https://raw.githubusercontent.com/aimlapi/api-docs/main/reference-files/blue-mug.jpg"
            ],
      guidance_scale: 19
    }),
  });

  const data = await response.json();
  console.log(data);
}

main();
```

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

<details>

<summary>Response</summary>

{% code overflow="wrap" %}

```json5
{
  "data": [
    {
      "url": "https://cdn.aimlapi.com/flamingo/files/b/0a847b4c/2-TzrE2bwvfldwD4O3aVi.png"
    }
  ],
  "meta": {
    "usage": {
      "tokens_used": 132300
    }
  }
}
```

{% endcode %}

</details>

<table data-full-width="true"><thead><tr><th width="442.0667724609375" valign="top">Reference Images</th><th valign="top">Generated Image</th></tr></thead><tbody><tr><td valign="top"><div><figure><img src="https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-fa899176b9c4b1be07345bfea5c792a8f6480c19%2Ft-rex%20(1).png?alt=media" alt=""><figcaption><p>Image #1</p></figcaption></figure></div></td><td valign="top"><div><figure><img src="https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-81b1e6fc6371463b9b850b400633d18924afd8cc%2F2-TzrE2bwvfldwD4O3aVi.png?alt=media" alt=""><figcaption><p><kbd><code>"Combine the images so the T-Rex is wearing a business suit, sitting in a cozy small café, drinking from the mug. Blur the background slightly to create a bokeh effect."</code></kbd></p></figcaption></figure></div></td></tr><tr><td valign="top"><div><figure><img src="https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-21cc1f1cf38504cb4c59d82e91dd184f26539e36%2Fblue-mug.jpg?alt=media" alt=""><figcaption><p>Image #2</p></figcaption></figure></div></td><td valign="top"></td></tr></tbody></table>

[^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.