---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: a photo of home
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 - reemas-irasna/home-decor_LoRA

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6638c2a3d5d215405e213a9b/6wr1qyRtCcdeMTVNqJghx.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6638c2a3d5d215405e213a9b/4xhhIJaZAFvmyls9Jb9wv.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6638c2a3d5d215405e213a9b/vY7jxRK7YbRaenJhHN4bs.png)


## Model description

These are reemas-irasna/home-decor_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 "a photo of home" to trigger the image generation.

## Download model

Weights for this model are available in Safetensors format.

[Download](reemas-irasna/home-decor_LoRA/tree/main) them in the Files & versions tab.



## Intended uses & limitations

#### How to use

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

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("reemas-irasna/home-decor_LoRA")
_ = pipe.to("cuda")

prompt = "a photo of bedroom in red and white combination"

image = pipe(prompt=prompt, num_inference_steps=25).images[0]
image
```