README.md

---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: cozy_stick
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
datasets:
- jtlowell/cozy_stickers
---
    
# LoRA DreamBooth - jtlowell/cozy_sticker

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

The weights were trained on the concept prompt: 

`cozy_stick`  

Use this keyword to trigger your custom model in your prompts. 

LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

## Usage

Make sure to upgrade diffusers to >= 0.19.0:
```
pip install diffusers --upgrade
```

In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark:
```
pip install invisible_watermark transformers accelerate safetensors
```

To just use the base model, you can run:

```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
)

# This is where you load your trained weights
pipe.load_lora_weights('jtlowell/cozy_sticker')

pipe.to("cuda")

prompt = "A majestic cozy_stick jumping from a big stone at night"

image = pipe(prompt=prompt, num_inference_steps=50).images[0]
```