Instructions to use mgnarag/jem-face-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mgnarag/jem-face-4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("mgnarag/jem-face-4", 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 mgnarag/jem-face-4: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/mgnarag/jem-face-4/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://mgnarag/jem-face-4/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgnarag/jem-face-4/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- c3d26d74bb508a15db53b3d1c53016d4a9603da3c4bd5ff209d6e2fb7ba246c5
- Size of remote file:
- 681 MB
- SHA256:
- 7b48db9116cb1284b798dc75910d63dccb05d67ba57ee2fd7e5f5d711f8a6d3d
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