---
base_model: Alpha-VLLM/Lumina-Image-2.0
library_name: diffusers
license: apache-2.0
instance_prompt: gh1b11 style
widget:
- text: howl phogh1b11 style
  output:
    url: image_0.png
- text: howl phogh1b11 style
  output:
    url: image_1.png
- text: howl phogh1b11 style
  output:
    url: image_2.png
- text: howl phogh1b11 style
  output:
    url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- lumina2
- lumina2-diffusers
- template:sd-lora
- text-to-image
- diffusers-training
- diffusers
- lora
- lumina2
- lumina2-diffusers
- template:sd-lora
---

<!-- 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. -->


# Lumina2 DreamBooth LoRA - Dino-LeeTaeHun/lumina2-ghibli-lora

<Gallery />

## Model description

These are Dino-LeeTaeHun/lumina2-ghibli-lora DreamBooth LoRA weights for Alpha-VLLM/Lumina-Image-2.0.

The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Lumina2 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_lumina2.md).


## Trigger words

You should use `gh1b11 style` to trigger the image generation.

The following `system_prompt` was also used used during training (ignore if `None`): None.

## Download model

[Download the *.safetensors LoRA](Dino-LeeTaeHun/lumina2-ghibli-lora/tree/main) in the Files & versions tab.

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

```py
TODO
```

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)


## 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]