Instructions to use vladocar/3dfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladocar/3dfood with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vladocar/3dfood", 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 vladocar/3dfood: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/vladocar/3dfood/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://vladocar/3dfood@refs/pr/2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vladocar/3dfood/resolve/refs%2Fpr%2F2/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 29dde0f9817567150901c58345b70e31eaee4ac3d338b3faf433f519e0a8b24d
- Size of remote file:
- 492 MB
- SHA256:
- 774a14e0a0956418239fc0839f0c678fdd1da4343ba21d3db8bd671583981ceb
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