Instructions to use Falah/wasma-agha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Falah/wasma-agha with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Falah/wasma-agha", 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 Falah/wasma-agha: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/Falah/wasma-agha/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Falah/wasma-agha/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Falah/wasma-agha/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 5b4e057986992dc59cf0cf0d799ca1e12e3487a8f2bd6fe2c7fc73c4e006c1f8
- Size of remote file:
- 246 MB
- SHA256:
- cae03f56145bc0da52c76a823d50e155e760f23a81a56b20f4174e102d9102ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.