---
license: apache-2.0
---

# Introduction

This is the example model of [this PR](https://github.com/okotaku/diffengine/pull/27).
The training is based on [DiffEngine](https://github.com/okotaku/diffengine), the open-source toolbox for training state-of-the-art Diffusion Models with diffusers and mmengine.

# Dataset

I used [diffusers/dog-example](https://huggingface.co/datasets/diffusers/dog-example).

# Inference

```
import torch
from diffusers import DiffusionPipeline

checkpoint = 'takuoko/small-sd-dreambooth-lora-dog'
prompt = 'A photo of sks dog in a bucket'

pipe = DiffusionPipeline.from_pretrained(
    'segmind/small-sd', torch_dtype=torch.float16)
pipe.to('cuda')
pipe.load_lora_weights(checkpoint, weight_name='pytorch_lora_weights.bin')

image = pipe(
    prompt,
    num_inference_steps=50,
).images[0]
image.save('demo.png')
```

# Example result

prompt = 'A photo of sks dog in a bucket'

![image](image0_step_999.png)
![image2](image1_step_999.png)
![image3](image2_step_999.png)
![image4](image3_step_999.png)