Instructions to use daeunni/CL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use daeunni/CL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("daeunni/CL") prompt = "a photo of sks teddybear" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 89389e86b85c926467007157a1baca82b1ebaacb1496d09547dfaa15e263e717
- Size of remote file:
- 16.2 MB
- SHA256:
- 428ef00c24f4e03b3a16f5117ad3206a8048249c2d306a80237a96eca8f1e049
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.