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---
library_name: diffusers
pipeline_tag: text-to-image
inference: true
base_model: stabilityai/sdxl-turbo
---
# DPO LoRA Stable Diffusion XL Turbo
Model trained with LoRA implementation of Diffusion DPO Read more [here](https://github.com/huggingface/diffusers/tree/main/examples/research_projects/diffusion_dpo)
Base Model: https://huggingface.co/stabilityai/sdxl-turbo
## Running with [🧨 diffusers library](https://github.com/huggingface/diffusers)
```python
from diffusers import DiffusionPipeline
from diffusers.utils import make_image_grid
import torch
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype=torch.float16, variant="fp16"
)
pipe.to("cuda")
pipe.load_lora_weights("radames/sdxl-turbo-DPO-LoRA", adapter_name="dpo-lora-sdxl-turbo")
pipe.set_adapters(["dpo-lora-sdxl-turbo"], adapter_weights=[1.0]) # you can play with adapter_weights to increase the effect of the LoRA model
seed = 123123
prompt = " A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece"
negative_prompt = "3d render, cartoon, drawing, art, low light, blur, pixelated, low resolution, black and white, old photo, blurry faces"
generator = torch.Generator().manual_seed(seed)
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=512,
height=512,
num_inference_steps=2,
generator=generator,
guidance_scale=1.0,
num_images_per_prompt=4
).images
make_image_grid(images, 1, 4)
```
## Guidance Scale vs LoRA weights
## Examples
Left Withoud DPO right with DPO LoRA
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