metadata

license: apache-2.0

Introduction

This is the example model of this PR. The training is based on DiffEngine, the open-source toolbox for training state-of-the-art Diffusion Models with diffusers and mmengine.

Dataset

I used 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'