Instructions to use willhx/train_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use willhx/train_lora 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("willhx/train_lora") prompt = "a photo of sofa" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download pytorch_lora_weights.bin from willhx/train_lora: direct link, hf CLI and curl.
- Browser
- Download file 3.29 MB
-
https://huggingface.co/willhx/train_lora/resolve/main/pytorch_lora_weights.bin
- Command line
-
hf download hf://willhx/train_lora/pytorch_lora_weights.bin
-
curl -L -o pytorch_lora_weights.bin https://huggingface.co/willhx/train_lora/resolve/main/pytorch_lora_weights.bin
3.29 MB
- Xet hash:
- 7b02c8aef41dc183d0a8246415c3d611811f56935e640f07a9bc563b1965a4ad
- Size of remote file:
- 3.29 MB
- SHA256:
- 4812c0faacfb842cce1b62127b0b570fcbedb78568ceaf9d0ee927516bad6b50
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.