metadata

base_model: FLUX
library_name: diffusers
license: other
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - flux
  - flux-diffusers
  - template:sd-lora
widget:
  - text: ' '
    output:
      url: image_0.png
  - text: ' '
    output:
      url: image_1.png
  - text: ' '
    output:
      url: image_2.png
  - text: ' '
    output:
      url: image_3.png

Flux DreamBooth LoRA - ashen0209/flux-lora-wlop

Model description

These are ashen0209/flux-lora-wlop LoRA weights for FLUX.

The model is trained based on https://huggingface.co/ashen0209/Flux-Dev2Pro (using this model to train a LoRA produces a better result), but please do not apply the LoRA on my trained model. Just use it on original Flux-dev.

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.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('ashen0209/flux-lora-wlop', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a portrait of a woman').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]