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: a tarot card, yarn art style
widget:
  - text: yoda, yarn art style
    output:
      url: yarn_art_1.png
  - text: cookie monster, yarn art style
    output:
      url: yarn_art_2.png
  - text: a dragon spewing fire, yarn art style
    output:
      url: yarn_art_3.png
  - text: albert einstein, yarn art style
    output:
      url: yarn_art_4.png
  - text: a panda riding a rocket, yarn art style
    output:
      url: yarn_art_5.png
  - text: the joker, yarn art style
    output:
      url: yarn_art_6.png

Flux DreamBooth LoRA - linoyts/yarn_art_flux_1_700_custom

Prompt
a dragon spewing fire, yarn art style

Prompt
a panda riding a rocket, yarn art style

Model description

These are linoyts/yarn_art_flux_1_700_custom 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 yarn art style 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/yarn_art_flux_1_700_custom', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a Yarn art style tarot card').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]