Instructions to use WALIDALI/bekiksrilym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WALIDALI/bekiksrilym 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/bekiksrilym", 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/bekiksrilym: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/WALIDALI/bekiksrilym/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://WALIDALI/bekiksrilym/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/WALIDALI/bekiksrilym/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- dcd50f9e57e25093e7957d09a370d969e36688b800af09917017eb08502e84e7
- Size of remote file:
- 246 MB
- SHA256:
- b95149a04da107cbc75b73a1d44ecdf686f508917a2bf5dda4ecf6bf2987ca44
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