Instructions to use jsugg/ggm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jsugg/ggm with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jsugg/ggm", 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 jsugg/ggm: direct link, hf CLI and curl.
- Browser
- Download file 681 MB
-
https://huggingface.co/jsugg/ggm/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jsugg/ggm/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jsugg/ggm/resolve/main/text_encoder/pytorch_model.bin
681 MB
- Xet hash:
- 30170a6408b5b3adb698c6179ca5aab22dff890e8d3c9b8fe98db23919239946
- Size of remote file:
- 681 MB
- SHA256:
- 27081cf1d96185db3f5ec01110ed5f6122ac830c3e1d2696e9ea9ee56207a262
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