Instructions to use ByteDance/Bernini-R-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ByteDance/Bernini-R-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-R-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
- Xet hash:
- 6b0f697ab37812cdde1efff750340be4bf0450712b37968180668adefde10bc2
- Size of remote file:
- 1 MB
- SHA256:
- 89e2087e3cf0c673d86f3a94966429d5dbbbb2bca023849c6b8508098793e028
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