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:
- 55ac6540fd0999f3b2665927c9ae205346d25f9fd56c626a79dce7bcdb379027
- Size of remote file:
- 13.1 MB
- SHA256:
- 8f307d5648383f7b14132f8c8afa4c90a329f0b5da452ba87971e3dce9403a84
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