Instructions to use birdaz/nagisa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use birdaz/nagisa with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("birdaz/nagisa", 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 birdaz/nagisa: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/birdaz/nagisa/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://birdaz/nagisa@refs/pr/2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/birdaz/nagisa/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- c0105f328389107f79dfce5a00568f7f18f3bf6356b18c2f0a4054603cf4500e
- Size of remote file:
- 246 MB
- SHA256:
- b945c63f6106041f1a6510f0a607349d028b5656cda1b16bb347a056958184ce
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.