Instructions to use dmmagdal/Inkpunk-Diffusion-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dmmagdal/Inkpunk-Diffusion-ONNX with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dmmagdal/Inkpunk-Diffusion-ONNX", 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 vae_encoder/model.onnx from dmmagdal/Inkpunk-Diffusion-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 137 MB
-
https://huggingface.co/dmmagdal/Inkpunk-Diffusion-ONNX/resolve/main/vae_encoder/model.onnx
- Command line
-
hf download hf://dmmagdal/Inkpunk-Diffusion-ONNX/vae_encoder/model.onnx
-
curl -L -o model.onnx https://huggingface.co/dmmagdal/Inkpunk-Diffusion-ONNX/resolve/main/vae_encoder/model.onnx
137 MB
- Xet hash:
- 51e6e45d9a91810d4e32011d7dd1684a87715cbb007b833d1924cc03463548d1
- Size of remote file:
- 137 MB
- SHA256:
- 5cb969aa4991771b50de6ad808ec06feacc3715f3fd09031550e0f12ec8abbe2
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