Instructions to use ezhoureal/aura_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ezhoureal/aura_style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ezhoureal/aura_style") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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# Flux Aura Style LoRA
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This repository contains
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## Files
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```text
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fal_flux2_edit_lora/
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pytorch_lora_weights.safetensors
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pytorch_lora_weights.diffusers.safetensors
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config_342740f8-8ed7-4b9a-88c3-7afb87679d59.json
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fal-training-result.json
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README.md
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training_data/
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train/
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metadata.jsonl
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pair-001.png ... pair-025.png
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conditioning/
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pair-001.png ... pair-025.png
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eval_data/
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eval01.jpg ... eval04.webp
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output_sync/
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stylized.png ... stylized04.png
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```
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The primary FAL-format adapter is:
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```text
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fal_flux2_edit_lora/pytorch_lora_weights.safetensors
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```
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A Diffusers-format converted copy is also included:
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```text
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fal_flux2_edit_lora/pytorch_lora_weights.diffusers.safetensors
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```
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## Training Details
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# Flux Aura Style LoRA
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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.
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## Training Details
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