Instructions to use zac/Turbo_Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zac/Turbo_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("stabilityai/sdxl-turbo", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("zac/Turbo_Lora") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- f73ab83d5a3450055463fdca6dffa8b2a8d27a8eb7dd038e2e2f97c7c22c5e32
- Size of remote file:
- 787 MB
- SHA256:
- a599c42a9f4f7494c7f410dbc0fd432cf0242720509e9d52fa41aac7a88d1b69
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.