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:
- 5bc61dd3f14aae58eb38fec7a31f50f5a567d8c663bd8eb335f96c59e1c39862
- Size of remote file:
- 2.82 GB
- SHA256:
- 20eb789667fa5e60e7516bf509512f6cb61f01b0aa0695eadaea930c13892b36
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