ezhoureal/flux_aura_style

Flux Aura Style LoRA

This repository contains a Flux-2 Edit LoRA adapter 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.

Files

fal_flux2_edit_lora/
  pytorch_lora_weights.safetensors
  pytorch_lora_weights.diffusers.safetensors
  config_342740f8-8ed7-4b9a-88c3-7afb87679d59.json
  fal-training-result.json
  README.md
training_data/
  train/
    metadata.jsonl
    pair-001.png ... pair-025.png
  conditioning/
    pair-001.png ... pair-025.png
eval_data/
  eval01.jpg ... eval04.webp
output_sync/
  stylized.png ... stylized04.png

The primary FAL-format adapter is:

fal_flux2_edit_lora/pytorch_lora_weights.safetensors

A Diffusers-format converted copy is also included:

fal_flux2_edit_lora/pytorch_lora_weights.diffusers.safetensors

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:

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 Flux-2 Edit compatible inference. A useful prompt pattern is:

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:

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.