Instructions to use allen93/lora_sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use allen93/lora_sample 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("allen93/lora_sample") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 62f33591af3e2e95f788e9e911fb538ac719517243fef9fa5c81116801b3ebf3
- Size of remote file:
- 6.59 MB
- SHA256:
- df6723573a624d4137375b4bf03c237505421e7c13749de5fc714c87de1113a6
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