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---
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'
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