Instructions to use ByteDance/Bernini-R-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Bernini-R-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ByteDance/Bernini-R-Diffusers", 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

- Xet hash:
- ce28fa45535b3d84dc4d7c5ed68752ef079279fff57f2d8760b6cc1b8e8f552d
- Size of remote file:
- 264 kB
- SHA256:
- 28ae3f129eef0395db0699fe631805fd1828277ee0ad403edcf2ffb53be0fdde
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