Instructions to use jzli/BRA-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jzli/BRA-v6 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jzli/BRA-v6", 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 vae/diffusion_pytorch_model.bin from jzli/BRA-v6: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/jzli/BRA-v6/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://jzli/BRA-v6/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/jzli/BRA-v6/resolve/main/vae/diffusion_pytorch_model.bin
335 MB
- Xet hash:
- 5390369ae289f5846ae14e43bb79dfba7c87c2b7ff58a98cf265d680aaf74d71
- Size of remote file:
- 335 MB
- SHA256:
- 5e686157b247cf05fa4a6cc2173b719b70fccdc26851b4a47f1b99de1adcfa68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.