Instructions to use allen93/lora_sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use allen93/lora_sample 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("allen93/lora_sample") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 35f1b4c2edc7403f7193511631ede6bc7b499975e8aa97f422b2cda2f7e1338c
- Size of remote file:
- 6.59 MB
- SHA256:
- c01cf8ae9e95e1e1ba52a8c8ee7be34401aa9f668f3005ae3a1965b9b0649aab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.