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:
- c576c0fb84e3d67a807348809ee18a72143e32a9def06e16207bca82ef96ffac
- Size of remote file:
- 6.59 MB
- SHA256:
- e36fd12f8eecba7e44a381e720b855f226cf7c863f4615316bcfde202dcddf58
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