Instructions to use tmklein/path-to-save-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tmklein/path-to-save-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("tmklein/path-to-save-model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 27d716e40e1af38f1aec353677c77c6eabd0a1e2513814ddbed21ba521954ab3
- Size of remote file:
- 6.59 MB
- SHA256:
- 87de0af443c36dcfda0afc1bbd0ae4043baedb5165271481367f184962fc5252
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