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 text_encoder/pytorch_model.bin from jzli/BRA-v6: direct link, hf CLI and curl.
- Browser
- Download file 492 MB
-
https://huggingface.co/jzli/BRA-v6/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://jzli/BRA-v6/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jzli/BRA-v6/resolve/main/text_encoder/pytorch_model.bin
492 MB
- Xet hash:
- 4fce7670ef9c85c25e7de84a2055dd6f2981848b3c9463f94625f3eb15117bbf
- Size of remote file:
- 492 MB
- SHA256:
- 6d93aad5e52d6fb201c9d040aba6bc56ba825ce12fe62f27be95b0903158e61c
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