Instructions to use public-data/Unique3D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use public-data/Unique3D with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("public-data/Unique3D", 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:
- aac4e78905f7ad407137bf68ebd10119ad9d207dbfa8591d1e84182fc71d7425
- Size of remote file:
- 1.45 GB
- SHA256:
- 845d3845053912728cd1453029a0ef87d3c0a3082a083ba393f36eaa5fb0e218
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