from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('flux_lora_takeout', weight_name='pytorch_lora_weights.safetensors')

image = pipeline('a photo').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]

nepyope/flux-lora-takeout

Flux DreamBooth LoRA - flux_lora_takeout

Model description

Trigger words

Download model

Use it with the 🧨 diffusers library

License

Intended uses & limitations

How to use

Limitations and bias

Training details

nepyope/
flux-lora-takeout