Instructions to use fal/Bernini-R-Aux-FlashPack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/Bernini-R-Aux-FlashPack with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/Bernini-R-Aux-FlashPack", 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
| tokenizer/spiece.model filter=lfs diff=lfs merge=lfs -text | |
| tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| assets/arena.png filter=lfs diff=lfs merge=lfs -text | |
| assets/bernini-icon.png filter=lfs diff=lfs merge=lfs -text | |
| vae/model.flashpack filter=lfs diff=lfs merge=lfs -text | |
| text_encoder/model.flashpack filter=lfs diff=lfs merge=lfs -text | |