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:
- c8519686216b30afc0f3f2e1bdaf46dcf1cd20ad7435d3beab814c2ace5284b2
- Size of remote file:
- 4.85 GB
- SHA256:
- dfb5f1a870a7f486c6ef8f48f4a4f0d906dfe379c4e6e3c3878c123753c6897e
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