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:
- 3e83ac51bff418ffa231ec3fe55b5a5e705a68b91d23c78abb0986940e955183
- Size of remote file:
- 6.59 MB
- SHA256:
- d411d607650665ef38ee4f91a11f3ded03db756f0843dc24d198657a0742cfe2
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