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:
- a8f9ee33138c5cc8698fdea0dd948fafe0dd4fde1f3398bf49657f46d930b232
- Size of remote file:
- 3.37 GB
- SHA256:
- a378c15cab68a45f7a8805dd69520c9139d9c795c9867d62532f702215dca2d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.