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license: apache-2.0 |
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# Introduction |
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This is the example model of [this PR](https://github.com/okotaku/diffengine/pull/27). |
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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. |
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# Dataset |
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I used [diffusers/dog-example](https://huggingface.co/datasets/diffusers/dog-example). |
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# Inference |
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``` |
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import torch |
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from diffusers import DiffusionPipeline |
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checkpoint = 'takuoko/small-sd-dreambooth-lora-dog' |
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prompt = 'A photo of sks dog in a bucket' |
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pipe = DiffusionPipeline.from_pretrained( |
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'segmind/small-sd', torch_dtype=torch.float16) |
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pipe.to('cuda') |
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pipe.load_lora_weights(checkpoint, weight_name='pytorch_lora_weights.bin') |
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image = pipe( |
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prompt, |
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num_inference_steps=50, |
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).images[0] |
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image.save('demo.png') |
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``` |
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# Example result |
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prompt = 'A photo of sks dog in a bucket' |
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