Instructions to use rodrigobrand/rodrigobrandt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rodrigobrand/rodrigobrandt with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rodrigobrand/rodrigobrandt", 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 rodrigobrand/rodrigobrandt: direct link, hf CLI and curl.
- Browser
- Download file 246 MB
-
https://huggingface.co/rodrigobrand/rodrigobrandt/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://rodrigobrand/rodrigobrandt/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rodrigobrand/rodrigobrandt/resolve/main/text_encoder/pytorch_model.bin
246 MB
- Xet hash:
- 05e7c543fd73df52bbc6ee6c83c8c93965e15ff2417f1467cbb5497edd35c28a
- Size of remote file:
- 246 MB
- SHA256:
- 3e3e05fa5e456f1093abab63cf9322b2d96e62b375b0049889f8bf0f763cbdb3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.