Instructions to use lora-library/https-huggingface-co-lora-library-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lora-library/https-huggingface-co-lora-library-test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lora-library/https-huggingface-co-lora-library-test") prompt = "Ping hair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from lora-library/https-huggingface-co-lora-library-test: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
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https://huggingface.co/lora-library/https-huggingface-co-lora-library-test/resolve/main/README.md
- Command line
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hf download hf://lora-library/https-huggingface-co-lora-library-test/README.md
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curl -L -o README.md https://huggingface.co/lora-library/https-huggingface-co-lora-library-test/resolve/main/README.md
713 Bytes
metadata
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: Ping hair
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA DreamBooth - bo
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on the instance prompt "Ping hair " using DreamBooth. You can find some example images in the following.



