---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK cat
widget: []
---

<!-- 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 - basakozsoy/maya_LoRA

<Gallery />

## Model description

These are basakozsoy/maya_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: True.

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

## Trigger words

You should use a photo of TOK cat to trigger the image generation.

## Download model

Weights for this model are available in Safetensors format.

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



## Intended uses & limitations

#### How to use

```python
import torch
from diffusers import DiffusionPipeline, AutoencoderKL

repo_id = 'basakozsoy/maya_LoRA'

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae,
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True
)
pipe.load_lora_weights(repo_id)
_ = pipe.to("cuda")

pipe.load_lora_weights(repo_id)
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]