from diffusers import FluxKontextPipeline
import torch
pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('mingyu-oo/FLUX_HD_ES', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('Designed by Hyundai, 3/4 front view, midsize sedan, three-box, balanced proportions, low stance, short overhangs, sleek beltline, clean body surfacing, angular shoulder lines, prominent grille, narrow intakes, sculpted hood, flush door handles, 19-inch multi-spoke wheels, low-profile tires, tinted glass, chrome accents, red paint').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]

mingyu-oo/FLUX_LoRA_adapter

Flux Kontext DreamBooth LoRA - mingyu-oo/FLUX_HD_ES

Model description

Trigger words

Download model

Use it with the 🧨 diffusers library

License

Intended uses & limitations

How to use

Limitations and bias

Training details

mingyu-oo/
FLUX_LoRA_adapter