---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: google illustration
widget:
- text: >-
    google illustration, orca leaping out of ocean, detailed, realistic, blue,
    white, and black
  output:
    url: images/example_zharhp46i.png
- text: google illustration, a coffee cup, geometric design, minimalistic, lines
  output:
    url: images/example_qgebiwrs1.png
- text: google illustration, a road along the sea shore, detailed, realistic
  output:
    url: images/example_c1z3wzaq2.png
- text: google illustration, a New York City skyline, detailed, realistic
  output:
    url: images/example_8anjglvlx.png
- text: >-
    google illustration, a car driving along the sea shore, sunny, detailed,
    realistic
  output:
    url: images/example_u0q14hmwe.png
- text: google illustration, a rainforest in the afternoon, photorealistic
  output:
    url: images/example_4gzk7t9br.png
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers

---

<!-- 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. -->


# SDXL LoRA DreamBooth - Google Illustrations

<Gallery />

## Model description

These are qingy2024/GI2_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

The weights were trained  using [DreamBooth](https://dreambooth.github.io/).

LoRA for the text encoder was enabled: False.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

## Trigger words

You should use google illustration to trigger the image generation.

## Download model

Weights for this model are available in Safetensors format.

[Download](qingy2024/GI2_LoRA/tree/main) them in the Files & versions tab.



## 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]