Instructions to use desimfj/V-Bridge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use desimfj/V-Bridge with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("desimfj/V-Bridge", 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:
- cd9b328448710e7d41c9e37fee039c76d5180922d382601faa9fc2b962303b78
- Size of remote file:
- 10 GB
- SHA256:
- 5e06200f5d2cec3cc2cbbffbc086212373682c50bbbfdfeadaf42cb8843a5f8b
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