Instructions to use luizC/Anine_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use luizC/Anine_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("luizC/Anine_model", 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 luizC/Anine_model: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/luizC/Anine_model/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://luizC/Anine_model/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/luizC/Anine_model/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- d4c7ff516ffb59f8e71e0072609e100cd9660fb37aa21406aa08e1fdd0dcb9cc
- Size of remote file:
- 492 MB
- SHA256:
- 404fb7e2ce341cbefd8da4f056eedd171844b201db20da7d0a3b87eb3b66bf8e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.