Instructions to use Dzoordan/lorry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Dzoordan/lorry with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Dzoordan/lorry") prompt = "A futuristic neon-lit cyberpunk city street where a sleek, chrome Lorry floats silently above a rain-slicked asphalt road." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- 8be51935b1a91bc82ffc362da9a22db81116c18d1525ee89992fe69f3be0f951
- Size of remote file:
- 1.41 MB
- SHA256:
- df53d77e2fcb0e203f53c3d9889871aeddcc35e4b55b14e5470e0b47c556d087
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.