Instructions to use ByteDance/Bernini-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Bernini-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-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
Ctrl+K
- 4.94 GB xet
- 4.74 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.94 GB xet
- 4.99 GB xet
- 4.95 GB xet
- 4.99 GB xet
- 4.95 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.85 GB xet
- 4.99 GB xet
- 4.94 GB xet
- 4.97 GB xet
- 4.9 GB xet
- 4.97 GB xet
- 940 MB xet
- 4.2 GB xet
- 4.2 GB xet
- 4.9 GB xet
- 4.99 GB xet
- 4.99 GB xet
- 4.93 GB xet
- 4.99 GB xet
- 4.99 GB xet
- 3.37 GB xet
- 308 kB