Instructions to use tangjs/uv-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tangjs/uv-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("SG161222/Realistic_Vision_V5.1_noVAE", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tangjs/uv-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 9c754b2917090394b14e42e502482375269c4ab3d3cf59c32405c6b22591f3bd
- Size of remote file:
- 6.47 MB
- SHA256:
- c6cf1e7eb6674474de2558ea056868c9d113000cad0a5e6ca018407f8e5bf460
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.