Instructions to use kxic/eschernet-4dof with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kxic/eschernet-4dof with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kxic/eschernet-4dof", 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 vae/diffusion_pytorch_model.bin from kxic/eschernet-4dof: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/kxic/eschernet-4dof/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://kxic/eschernet-4dof/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/kxic/eschernet-4dof/resolve/main/vae/diffusion_pytorch_model.bin
167 MB
- Xet hash:
- 7446a37a190e1caf0110d0e8fd102ac923c61cb527930180f8e745ffd1547c86
- Size of remote file:
- 167 MB
- SHA256:
- 3ed683646d23a1f569ff9b8b40e923a82b53431e1b15f5eae61b54600767e2d2
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