---
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
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# Flux DreamBooth LoRA - linoyts/yarn_art_flux_1_700_custom

<Gallery />

## 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](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).

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](linoyts/yarn_art_flux_1_700_custom/tree/main) in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
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](https://huggingface.co./docs/diffusers/main/en/using-diffusers/loading_adapters)

## License

Please adhere to the licensing terms as described [here](https://huggingface.co./black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).


## Intended uses & limitations

#### How to use

```python
# 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]