Instructions to use lora-library/wyt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lora-library/wyt 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/wyt") prompt = "wangyanting" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-1400/pytorch_model.bin from lora-library/wyt: direct link, hf CLI and curl.
- Browser
- Download file 3.42 MB
-
https://huggingface.co/lora-library/wyt/resolve/main/checkpoint-1400/pytorch_model.bin
- Command line
-
hf download hf://lora-library/wyt/checkpoint-1400/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lora-library/wyt/resolve/main/checkpoint-1400/pytorch_model.bin
3.42 MB
- Xet hash:
- 522275fb7dce8dae12112b27222b75655ff6db5b733b547bd0a888984443f89a
- Size of remote file:
- 3.42 MB
- SHA256:
- 66a8c125d896884fa8c0c48c04e9956132e2e7c7311f4ed21fbdade603ab47c4
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