Instructions to use zimhe/SpatialDiffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zimhe/SpatialDiffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zimhe/SpatialDiffusion", 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:
- 87a9da12d8783385d08d7bb529f0f53e6b4bf32469d28c81e7be91bcebb15897
- Size of remote file:
- 3.44 GB
- SHA256:
- 07a3d61e84dc96ec2511833ff72ed5ab07bc1e0d1d3e077303d0472a928ecf51
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