metadata
base_model: black-forest-labs/FLUX.2-klein-base-4B
library_name: diffusers
license: other
instance_prompt: lghtlm style
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux2-klein
- flux2-klein-diffusers
- template:sd-lora
Flux.2 [Klein] DreamBooth LoRA - Efradeca/lightloom-style-lora
Model description
These are Efradeca/lightloom-style-lora DreamBooth LoRA weights for black-forest-labs/FLUX.2-klein-base-4B.
The weights were trained using DreamBooth with the Flux2 diffusers trainer.
Quant training? None
Trigger words
You should use lghtlm style to trigger the image generation.
Download model
Download the *.safetensors LoRA in the Files & versions tab.
Use it with the 🧨 diffusers library
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.2", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('Efradeca/lightloom-style-lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('lghtlm style').images[0]
For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers
License
Please adhere to the licensing terms as described here.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]