Instructions to use EarthnDusk/NegativeEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EarthnDusk/NegativeEmbed with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_textual_inversion("EarthnDusk/NegativeEmbed") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26a5210d90664eabdff67f9d58edfad0a3252af7c22fd38a9792fdb7e62af908
- Size of remote file:
- 13.3 kB
- SHA256:
- 7357e000ea6778ab04e155c11abca4e1b52c7614dfcf744f098d598661126926
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.