Instructions to use jiangdaniel/dog_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jiangdaniel/dog_lora 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("jiangdaniel/dog_lora") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- ce55dfce7739e1c16b0ba2305cd5b2aeed030dcc3c43b089593b9153e5a06463
- Size of remote file:
- 1 kB
- SHA256:
- 775169a85d4b90dd73a828433c824604dc39dbd36cf213d90c3e0c82687b3460
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.