README.md

metadata

license: other
library_name: diffusers
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - flux
  - flux-diffusers
  - template:sd-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: an tok dog
widget: []

Flux DreamBooth LoRA - linoyts/dog_flux_lora

Model description

These are linoyts/dog_flux_lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.

The weights were trained using DreamBooth with the Flux diffusers trainer.

Was LoRA for the text encoder enabled? False.

Trigger words

You should use an tok dog 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.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('linoyts/dog_flux_lora', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('an tok dog').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]