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:
- 28e97f60aa4f13a1b8ae82f89f2a4ef5384989a2306491cb197e45e6aa28a5a0
- Size of remote file:
- 4.95 GB
- SHA256:
- 36382f7d6b2ba35527c9be876819b43777ee1f063a103f2940982f61528c0fb0
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