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:
- a05172aa5eec75874f86755b89dda3b89b373edbc18295294aeb7c71e5e2d970
- Size of remote file:
- 6.59 MB
- SHA256:
- 8e308f5c0703106c8b6510a5d4d71e8625ecbf18a360dd9655fcde8e75e231f9
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