Instructions to use ByteDance/Bernini-Diffusers-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Bernini-Diffusers-v2 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-Diffusers-v2", 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:
- 6efbb1434f4c832a76745215967e3b7f5db3e0ef2ba0d6f0586a9e4dd807fe98
- Size of remote file:
- 4.85 GB
- SHA256:
- a43b48b62eb53255ab14fad4dd035b5a0db13e1fdff5daa3480394eaa40079d6
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