Instructions to use LudwigZ/output_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LudwigZ/output_model 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", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("LudwigZ/output_model") prompt = "a jk avatar" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ebb638b49b189fa1ee95167d8b922ea17da92d7b1aa4d8b0b91530a00aac1fe7
- Size of remote file:
- 6.59 MB
- SHA256:
- 6d3841846b6ed9f27187de7496b05fe2afd33b1115741f6beab5448c0c2dfbc8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.