Instructions to use stablediffusionapi/mverug7bhds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stablediffusionapi/mverug7bhds with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stablediffusionapi/mverug7bhds", 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 stablediffusionapi/mverug7bhds: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/stablediffusionapi/mverug7bhds/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://stablediffusionapi/mverug7bhds/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stablediffusionapi/mverug7bhds/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 288f359ca587a65b607dd1f75bebdc071ae7f97ba97bca8ef2d362b5b8b11df7
- Size of remote file:
- 246 MB
- SHA256:
- 0cc8861a6234b089b40b6e226fb0fbdb6c9d70077a2ceb792c82bd5fcc566c0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.