Instructions to use eagle13gy/path_to_lora_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use eagle13gy/path_to_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("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("eagle13gy/path_to_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:
- 8e3027ed4d389a300c6d32d09d9f3490f6f965644ffa41fe2a0ec6e961913897
- Size of remote file:
- 3.29 MB
- SHA256:
- 2aeea6cbfef2fd8ca835ebd632adc1442b4ea80774cc0914513de1ec344f9eb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.