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---
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. -->
Using Stable Diffusion XL 1.5, trained a custom model for image generation of Home Decor usecase
Also applied Canny ControlNets to it for better performance.
Training Notebook - https://www.kaggle.com/code/reemasirasna/sdxl-training-decor
ControlNet Notebook - https://www.kaggle.com/code/reemasirasna/sdxl-controlnet-decor
Below are the generated images from this model.
# SDXL LoRA DreamBooth - reemas-irasna/home-decor_LoRA
## 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
```
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