README.md

metadata

license: other
library_name: diffusers
tags:
  - text-to-image
  - diffusers-training
  - diffusers
  - lora
  - replicate
  - flux
  - flux-diffusers
  - template:sd-lora
base_model: FLUX.1-dev
instance_prompt: a photo of the FTR170 building
widget: []

Flux DreamBooth LoRA - ell-hol/FTR170-flux-dbth-lr

Model description

These are ell-hol/FTR170-flux-dbth-lr DreamBooth LoRA weights for FLUX.1-dev.

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

Was LoRA for the text encoder enabled? False.

Trigger words

You should use a photo of the FTR170 building 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('ell-hol/FTR170-flux-dbth-lr', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of the FTR170 building').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]