Instructions to use lora-library/the-crystal-exarch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lora-library/the-crystal-exarch 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/the-crystal-exarch") prompt = "FantasyMiq" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/pytorch_model.bin from lora-library/the-crystal-exarch: direct link, hf CLI and curl.
- Browser
- Download file 3.42 MB
-
https://huggingface.co/lora-library/the-crystal-exarch/resolve/main/checkpoint-500/pytorch_model.bin
- Command line
-
hf download hf://lora-library/the-crystal-exarch/checkpoint-500/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lora-library/the-crystal-exarch/resolve/main/checkpoint-500/pytorch_model.bin
3.42 MB
- Xet hash:
- 706d8637c23a6ec101c2e9c86de7d96043d1c20a17871e55eac4ab4653a67005
- Size of remote file:
- 3.42 MB
- SHA256:
- be257652e8fcc841841da11fd7d6c2b266873255844c56acdeab470ad34c9ac1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.