---
license: apache-2.0
base_model:
- black-forest-labs/FLUX.2-dev
- black-forest-labs/FLUX.2-klein-4B
- stabilityai/stable-diffusion-3.5-medium
library_name: diffusers
pipeline_tag: image-to-image
tags:
- flux
- flux-2
- flux-2-edit
- lora
- image-to-image
- image-editing
- style-transfer
- aura-style
datasets:
- ezhoureal/aura_style
---

# Flux Aura Style LoRA

This repository contains LoRA adapters for applying a radiant aura style to image edits. The style emphasizes smooth colorful gradients, ethereal haze, subtle contour lighting, and a polished cinematic glow while aiming to preserve the source subject and composition.


## Training Details

- Base/edit model: `black-forest-labs/FLUX.2-dev` / Flux-2 Edit
- Training service: `fal-ai/flux-2/lora/edit`
- Steps: 1,000
- Learning rate: `5e-5`
- Dataset: 25 paired image-edit examples
- Training data size: 25 target images, 25 conditioning/source images, and metadata
- Output style: radiant aura lighting, smooth colorful gradients, haze, contour highlights, cinematic glow

Default training instruction:

```text
Apply a radiant aura lighting style with smooth colorful gradients, ethereal haze, subtle contour lighting, and a refined cinematic glow while preserving the subject and composition.
```

## Usage

Use the adapter with compatible Image2Image inference. A useful prompt pattern is:

```text
Transform this photorealistic image into the trained radiant aura style: smooth colorful gradients, ethereal haze, subtle contour lighting, and a refined cinematic glow. Preserve the subject identity, composition, pose, silhouette, camera framing, and important details.
```

Suggested starting settings:

- LoRA scale: `1.0`
- Guidance scale: `2.5`
- Inference steps: `28`

For local Diffusers workflows, load the Diffusers-format adapter:

```python
from diffusers import Flux2Pipeline

pipe = Flux2Pipeline.from_pretrained("diffusers/FLUX.2-dev-bnb-4bit")
pipe.load_lora_weights(
    "fal_flux2_edit_lora/pytorch_lora_weights.diffusers.safetensors",
    adapter_name="aura",
)
pipe.set_adapters(["aura"], adapter_weights=[1.0])
```

## Evaluation Assets

`eval_data/` contains the input images used for evaluation. `output_sync/` contains corresponding stylized outputs generated with the uploaded adapter.

## Intended Use

This LoRA is intended for stylized image editing where the source image should remain recognizable while receiving a luminous aura treatment. It works best with clear subjects, simple-to-medium complexity compositions, and prompts that explicitly preserve identity, pose, framing, and important details.

## Limitations

- The adapter is style-focused and may over-apply glow or color gradients at high LoRA scales.
- The training set is compact, so unusual image domains may need prompt tuning or a lower adapter strength.
- It inherits behavior, restrictions, and access requirements from the Flux-2 Edit base model.
- The dataset and outputs are included for reproducibility and inspection.