Instructions to use huangdffs/animef32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use huangdffs/animef32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("huangdffs/animef32", 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
Download traindata.pt from huangdffs/animef32: direct link, hf CLI and curl.
- Browser
- Download file 156 MB
-
https://huggingface.co/huangdffs/animef32/resolve/main/traindata.pt
- Command line
-
hf download hf://huangdffs/animef32/traindata.pt
-
curl -L -o traindata.pt https://huggingface.co/huangdffs/animef32/resolve/main/traindata.pt
156 MB
- Xet hash:
- 7c780e6238e8a81aec328a53c87fd4870fdf1e01fb5cee1d8435b81beb76ebe5
- Size of remote file:
- 156 MB
- SHA256:
- aadb6ca9f0564b3ca4c818d28e74ed6b6f778426d278818ba430369ad0f4b618
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.