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", torch_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:
- e3c4e9f877af47512d512f0a88baee263eb2268225426e06beeb4027fae0cf51
- Size of remote file:
- 4.95 GB
- SHA256:
- 33a109b49e7181e53c43dff25f8fa1a0b33ed181bea82106335086b493413cc0
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