Instructions to use Shirmil/lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shirmil/lora_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("Shirmil/lora_model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 608eae463b71b8bdd91ace1fc6f7b6e64fac10a75ea17493efeba15ec87e860f
- Size of remote file:
- 6.58 MB
- SHA256:
- 6586170fb3da1bd465dbc3ab0df69f87d0675b16514a062e67577ac79bd5c0f4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.