Instructions to use bariscal/cbst_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bariscal/cbst_style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bariscal/cbst_style", 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 model.ckpt from bariscal/cbst_style: direct link, hf CLI and curl.
- Browser
- Download file 2.58 GB
-
https://huggingface.co/bariscal/cbst_style/resolve/main/model.ckpt
- Command line
-
hf download hf://bariscal/cbst_style/model.ckpt
-
curl -L -o model.ckpt https://huggingface.co/bariscal/cbst_style/resolve/main/model.ckpt
2.58 GB
- Xet hash:
- a0b9e1d9af3bb13381bf201471a7ce1457997289ca09c76ea22c8c3e1753bfef
- Size of remote file:
- 2.58 GB
- SHA256:
- d91769ebf34e364e28cfe43fd83b278bb386e3a897b68864b0ccea291bc6ce9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.