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
- Xet hash:
- 21bd7dea69f8dd479471d82cccd943c56e01eb751c9718dd39154f27dea99d0c
- Size of remote file:
- 11.4 GB
- SHA256:
- 8d5e1ad4123fd2c72f643173cc7362162b55752f94ed0bd04abc139cf91cfe23
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