Instructions to use hf-internal-testing/lora_dreambooth_dog_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/lora_dreambooth_dog_example with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hf-internal-testing/lora_dreambooth_dog_example") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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---
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license: creativeml-openrail-m
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base_model: runwayml/stable-diffusion-v1-5
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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inference: true
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---
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# LoRA DreamBooth - https://huggingface.co/patrickvonplaten/dummy
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These are LoRA adaption weights for https://huggingface.co/patrickvonplaten/dummy. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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