Instructions to use WALIDALI/bekinorrev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WALIDALI/bekinorrev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WALIDALI/bekinorrev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from WALIDALI/bekinorrev: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/WALIDALI/bekinorrev/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://WALIDALI/bekinorrev/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/WALIDALI/bekinorrev/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- bc42cd88cc9296ed4f93dfcda70dcb9e9c3dd861787e967a9b137b63b3c8aa3a
- Size of remote file:
- 246 MB
- SHA256:
- a5ee84a4a915fc09028771c73179aad89dcfcecf7a4cf10dcc828512286fd62e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.