Instructions to use debadas/ronaldo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use debadas/ronaldo 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("debadas/ronaldo") prompt = "a photo of sks man" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- d450d2ab01d8ee4005bbed781044a011c42c8273cf8bd74050c54d478a8d3edc
- Size of remote file:
- 6.59 MB
- SHA256:
- 1f89066471efcea5eab8de6562d8694b58c75f77ca2d4d9eb05ca92a613438a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.