Instructions to use dmillar/wsj-hedcut-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dmillar/wsj-hedcut-v1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dmillar/wsj-hedcut-v1", 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 dmillar/wsj-hedcut-v1: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/dmillar/wsj-hedcut-v1/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://dmillar/wsj-hedcut-v1/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dmillar/wsj-hedcut-v1/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- b4dd2c11422548d8168e25125c367b67de34469dd34a819a9f902a1b21279d03
- Size of remote file:
- 246 MB
- SHA256:
- 01ae3d0cf05f4390b227d4c0278973c3ac5fb7cfdd855429eb6f0d393ff2be89
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